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Development of a BIM-enabled and cloud-based sustainability assessment system for buildings in sub-Saharan Africa: the case of Nigeria

Development of a BIM-enabled and cloud-based sustainability assessment system for buildings in sub-Saharan Africa: the case of Nigeria

Profile image of Dr. Timothy O OlawumiDr. Timothy O Olawumi

2020, PhD Thesis. Hong Kong Polytechnic University

The increasing urbanization of the built environment has bolstered the need of promoting sustainable practices and Building Information Modelling (BIM) initiative in building and construction projects. However, there has not been a unified adoption and implementation of BIM initiative and sustainability in most countries and the built environment as a whole – most notably within the sub-Saharan region of Africa due to several factors. Moreover, based on the extant literature, the existing green rating tools have been found to be inadequate to fully address the greenness and evaluate the sustainability performance of buildings. Hence, these generate several hindrances to the current drive for a holistic implementation of sustainability practices and innovative technologies such as BIM in the construction industry. Therefore, this research study aims to develop a green-BIM assessment model and cloud-based sustainability decision support system for evaluating buildings' compliance to sustainability principles with a view to integrating smart sustainable practices in building construction and management, improving operational efficiency, and enhancing the overall implementation of sustainable development in the built environment. The scope of study mainly focuses on developing countries located in the sub-Saharan region of Africa – using Nigeria, the largest economy in the region as a case study – with practical applications to other regions. The following research objectives was set out in fulfilling the study’s aim: (1) To identify and assess the inherent benefits, barriers, and critical success factors (drivers) associated with integrating BIM and sustainability principles in building projects. (2) To establish the relative weightings of the key sustainability indicators, sustainability attributes, and sub-attributes for buildings. (3) To develop a sustainability evaluation index for buildings using the Generalized Choquet Fuzzy Integral method. (4) To develop a cloud-based sustainability decision support system (C-SDSS) for buildings. (5) To develop a conceptual Green-BIM assessment framework as a tool for the evaluation of sustainability performance of buildings. Objective #1 was achieved via an in-depth desktop review of extant literature, pilot and Delphi surveys, empirical questionnaire surveys, as well as the use of several statistical analysis tools such as descriptive and inferential statistical tools, factor analysis, and fuzzy synthesis evaluation method. According to the desktop literature review, a total of 36 benefits, 38 barriers and 30 critical success factors were identified. Fourteen (14) experts from eight countries participated in the Delphi survey while 220 respondents from 21 countries were involved in the international questionnaire survey. Meanwhile, the base inputs for the data to achieve Objectives (#2 - #4) consisted of 189 diversified sets of experts in Nigeria with requisite experience in the built environment. A holistic review of green building technical notes and guidelines, existing green building rating systems, and relevant journal articles was undertaken to fulfil Objectives (#2 - #4); and augmented by industry experts’ inputs which facilitated the development of the Building Sustainability Assessment Method (BSAM) scheme. The BSAM scheme green rating system has been designed for developing countries in the sub-Saharan region of Africa. The proposed BSAM scheme is a more unified green rating tool that adequately considers the environmental, economic, and social pillars of sustainable development unlike the existing green rating tools such as LEED, BREEAM, BEAM Plus, Green Mark, etc. which focus solely on the environmental sustainability with little or no consideration of the other two sustainability pillars. Objective #3 was actualized by employing the Generalized Choquet Fuzzy Integral (GCFI) method to establish the weightings of the 8 key sustainability indicators, 32 sustainability attributes, and 136 sustainability sub-attributes of the BSAM scheme. Data collected from industry experts form the base inputs for the impacts of various sustainability criteria based on the local variations. The GCFI approach is regarded as a more practical and robust weighting method for non-additive, dependent, and interactive criteria. Consequently, the Building Sustainability Evaluation Index (BSEI) and a six-grade certification system were developed to evaluate the sustainability performance of building projects. The key sustainability criteria with the highest weighting based on the GCFI analysis include sustainability construction practices, transportation, and energy criteria. To ease the adoption and implementation of the proposed BSAM scheme, BSEI, and the BSAM certification system for use in the built environment, a Cloud-Based Sustainability Decision Support System (C-SDSS) was established to achieve Objective #4. PHP and Jscript, being high-level programming languages, as well as the MySQL relational database along with other web-based tools and systems were used to code, design, and deploy the C-SDSS platform. BIM models, and relevant data from four real-life building projects based in Nigeria were used during the validation exercise to demonstrate the usefulness of the developed C-SDDS and the BSAM scheme in practice for the built environment. The validation exercise was augmented with validation questionnaire surveys with industry experts. Finally, a conceptual green-BIM assessment (GBA) framework was developed as an effective tool for the evaluation of the sustainability performance of buildings using a cloud-based system (Objective #5). The proposed GBA framework comprised of six main components is intended to provide comprehensive guidelines and algorithms that can facilitate the full and optimal integration of BIM and green building rating systems (e.g. the proposed BSAM scheme) in the assessment of the sustainability performance of buildings. The developed GBA framework was validated using expert questionnaire surveys as well. The findings of the study have generated salient and significant contributions both from the theoretical and practical (industry) standpoints. Moreover, they have provided valuable insights, effective strategies, and recommendations that have addressed the limitations of the integration of the concepts of BIM and sustainability practices in the built environment. Overall, the research deliverables would be crucial in implementing Green-BIM both locally (in Nigeria and other sub-Saharan countries), and internationally.

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Copyright Undertaking This thesis is protected by copyright, with all rights reserved. By reading and using the thesis, the reader understands and agrees to the following terms: 1. The reader will abide by the rules and legal ordinances governing copyright regarding the use of the thesis. 2. The reader will use the thesis for the purpose of research or private study only and not for distribution or further reproduction or any other purpose. 3. The reader agrees to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage. IMPORTANT If you have reasons to believe that any materials in this thesis are deemed not suitable to be distributed in this form, or a copyright owner having difficulty with the material being included in our database, please contact lbsys@polyu.edu.hk providing details. The Library will look into your claim and consider taking remedial action upon receipt of the written requests. Pao Yue-kong Library, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong http://www.lib.polyu.edu.hk DEVELOPMENT OF A BIM-ENABLED AND CLOUD-BASED SUSTAINABILITY ASSESSMENT SYSTEM FOR BUILDINGS IN SUBSAHARAN AFRICA: THE CASE OF NIGERIA OLAWUMI TIMOTHY OLUWATOSIN PhD The Hong Kong Polytechnic University 2020 The Hong Kong Polytechnic University Department of Building and Real Estate DEVELOPMENT OF A BIM-ENABLED AND CLOUD-BASED SUSTAINABILITY ASSESSMENT SYSTEM FOR BUILDINGS IN SUBSAHARAN AFRICA: THE CASE OF NIGERIA OLAWUMI Timothy Oluwatosin A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy February 2020 CERTIFICATE OF ORIGINALITY I hereby declare that this thesis is my own work and that, to the best of my knowledge and belief, it reproduces no material previously published or written, nor material that has been accepted for the award of any other degree or diploma, except where due acknowledgement has been made in the text. __________________________________ (Signed) ___OLAWUMI Timothy Oluwatosin_____ (Name of student) i DEDICATION I dedicate this thesis to the Trinity God – God Almighty, Jesus Christ, and the Holy Spirit for the life, health, grace, and the strength to successfully complete this study. Also, to my parents – Dr. Simeon O. Olawumi and Mrs. Ireade I. Olawumi for their prayers and supports towards my education pursuits from the cradle. To my Soulmate and lovely wife, Mrs. Taiwo Oluwafunke Olawumi and my wonderful son, Master Josiah Oluwadara Olawumi, for their prayers, support, and sacrifice during my PhD study. ii ABSTRACT The increasing urbanization of the built environment has bolstered the need of promoting sustainable practices and Building Information Modelling (BIM) initiative in building and construction projects. However, there has not been a unified adoption and implementation of BIM initiative and sustainability in most countries and the built environment as a whole – most notably within the sub-Saharan region of Africa due to several factors. Moreover, based on the extant literature, the existing green rating tools have been found to be inadequate to fully address the greenness and evaluate the sustainability performance of buildings. Hence, these generate several hindrances to the current drive for a holistic implementation of sustainability practices and innovative technologies such as BIM in the construction industry. Therefore, this research study aims to develop a green-BIM assessment model and cloud-based sustainability decision support system for evaluating buildings' compliance to sustainability principles with a view to integrating smart sustainable practices in building construction and management, improving operational efficiency, and enhancing the overall implementation of sustainable development in the built environment. The scope of study mainly focuses on developing countries located in the sub-Saharan region of Africa – using Nigeria, the largest economy in the region as a case study – with practical applications to other regions. The following research objectives was set out in fulfilling the study’s aim: (1) To identify and assess the inherent benefits, barriers, and critical success factors (drivers) associated with integrating BIM and sustainability principles in building projects. (2) To establish the relative weightings of the key sustainability indicators, sustainability attributes, and sub-attributes for buildings. (3) To develop a sustainability evaluation index for buildings using the Generalized Choquet Fuzzy Integral method. (4) To develop a cloud-based sustainability decision support system (C-SDSS) for buildings. (5) To develop a conceptual Green-BIM assessment framework as a tool for the evaluation of sustainability performance of buildings. Objective #1 was achieved via an in-depth desktop review of extant literature, pilot and Delphi surveys, empirical questionnaire surveys, as well as the use of several statistical analysis tools such as descriptive and inferential statistical tools, factor analysis, and fuzzy synthesis evaluation method. According to the desktop literature iii review, a total of 36 benefits, 38 barriers and 30 critical success factors were identified. Fourteen (14) experts from eight countries participated in the Delphi survey while 220 respondents from 21 countries were involved in the international questionnaire survey. Meanwhile, the base inputs for the data to achieve Objectives (#2 - #4) consisted of 189 diversified sets of experts in Nigeria with requisite experience in the built environment. A holistic review of green building technical notes and guidelines, existing green building rating systems, and relevant journal articles was undertaken to fulfil Objectives (#2 - #4); and augmented by industry experts’ inputs which facilitated the development of the Building Sustainability Assessment Method (BSAM) scheme. The BSAM scheme green rating system has been designed for developing countries in the sub-Saharan region of Africa. The proposed BSAM scheme is a more unified green rating tool that adequately considers the environmental, economic, and social pillars of sustainable development unlike the existing green rating tools such as LEED, BREEAM, BEAM Plus, Green Mark, etc. which focus solely on the environmental sustainability with little or no consideration of the other two sustainability pillars. Objective #3 was actualized by employing the Generalized Choquet Fuzzy Integral (GCFI) method to establish the weightings of the 8 key sustainability indicators, 32 sustainability attributes, and 136 sustainability sub-attributes of the BSAM scheme. Data collected from industry experts form the base inputs for the impacts of various sustainability criteria based on the local variations. The GCFI approach is regarded as a more practical and robust weighting method for non-additive, dependent, and interactive criteria. Consequently, the Building Sustainability Evaluation Index (BSEI) and a six-grade certification system were developed to evaluate the sustainability performance of building projects. The key sustainability criteria with the highest weighting based on the GCFI analysis include sustainability construction practices, transportation, and energy criteria. To ease the adoption and implementation of the proposed BSAM scheme, BSEI, and the BSAM certification system for use in the built environment, a Cloud-Based Sustainability Decision Support System (C-SDSS) was established to achieve Objective #4. PHP and Jscript, being high-level programming languages, as well as the MySQL relational database along with other web-based tools and systems were iv used to code, design, and deploy the C-SDSS platform. BIM models, and relevant data from four real-life building projects based in Nigeria were used during the validation exercise to demonstrate the usefulness of the developed C-SDDS and the BSAM scheme in practice for the built environment. The validation exercise was augmented with validation questionnaire surveys with industry experts. Finally, a conceptual green-BIM assessment (GBA) framework was developed as an effective tool for the evaluation of the sustainability performance of buildings using a cloud-based system (Objective #5). The proposed GBA framework comprised of six main components is intended to provide comprehensive guidelines and algorithms that can facilitate the full and optimal integration of BIM and green building rating systems (e.g. the proposed BSAM scheme) in the assessment of the sustainability performance of buildings. The developed GBA framework was validated using expert questionnaire surveys as well. The findings of the study have generated salient and significant contributions both from the theoretical and practical (industry) standpoints. Moreover, they have provided valuable insights, effective strategies, and recommendations that have addressed the limitations of the integration of the concepts of BIM and sustainability practices in the built environment. Overall, the research deliverables would be crucial in implementing Green-BIM both locally (in Nigeria and other sub-Saharan countries), and internationally. v LIST OF RESEARCH PUBLICATIONS AND ACHIEVEMENTS Chapters of this thesis have been fully or partially published in the following publications which spans from September 2016 – August 2020. Refereed Journal Papers (published) 1. Olawumi, T.O. & Chan, D.W.M. (2020). Application of Generalized Choquet Fuzzy Integral Method in the Sustainability Rating of Green Buildings Based on the BSAM Scheme. Sustainable Cities and Society, 61(October), Article Number 102147. 18 Pages. https://doi.org/10.1016/j.scs.2020.102147. [WoS Impact Factor: 5.268; Scopus CiteScore: 7.5 (top 98th percentile)] 2. Olawumi, T.O., Chan, D.W.M., Chan, A.P.C. & Wong, J.K.W. (2020). Development of a Building Sustainability Assessment Method (BSAM) for Developing Countries in Sub-Saharan Africa. Journal of Cleaner Production, 263 (August), Article Number 121514. https://doi.org/10.1016/j.jclepro.2020.121514 17 Pages. [WoS Impact Factor: 7.246; Scopus CiteScore: 10.9 (top 98th percentile)] 3. Olawumi, T.O. & Chan, D.W.M. (2020). Key Drivers for Smart and Sustainable Practices in the Built Environment. Engineering, Construction and Architectural Management, 27(6), 1257–1281. https://doi.org/10.1108/ECAM-06-2019-0305 [WoS Impact Factor: 2.16; Scopus CiteScore: 2.51 (top 95th percentile)] 4. Olawumi, T.O. & Chan, D.W.M. (2020). Concomitant Impediments to the Implementation of Smart Sustainable Practices in the Built Environment. Sustainable Production and Consumption, https://doi.org/10.1016/j.spc.2019.09.001 21(January), 239-251. [WoS Impact Factor: 3.66; Scopus CiteScore: 5.1 (top 84th percentile)] 5. Chan, D.W.M., Olawumi, T.O. & Ho, A.M.L. (2019). Critical Success Factors for Building Information Modelling (BIM) Implementation in Hong Kong. Engineering, Construction and Architectural Management, https://doi.org/10.1108/ECAM-05-2018-0204 26(9), 1838-1854. [WoS Impact Factor: 2.16; Scopus CiteScore: 2.51 (top 95th percentile)] 6. Chan, D.W.M., Olawumi, T.O. & Ho, A.M.L. (2019). Perceived Benefits of and Barriers to Building Information Modelling (BIM) Implementation in Construction: The Case of Hong Kong. Journal of Building Engineering, 25(September), Article vi Number 100764, 10 Pages. https://doi.org/10.1016/j.jobe.2019.100764 [WoS Impact Factor: 3.379; Scopus CiteScore: 4.9 (top 97th percentile)] 7. Olawumi, T.O. & Chan, D.W.M. (2019). Critical Success Factors for Implementing Building Information Modeling and Sustainability Practices in Construction Projects: A Delphi Survey. Sustainable Development, 27(4), 587602. https://doi.org/10.1002/sd.1925 [WoS Impact Factor: 4.082; Scopus CiteScore: 4.9 (top 92nd percentile)] 8. Olawumi, T.O. & Chan, D.W.M. (2019). An Empirical Survey of the Perceived Benefits of Executing BIM and Sustainability Practices in the Built Environment. Construction Innovation: Information, Process, Management, 19(3), 321-342. https://doi.org/10.1108/CI-08-2018-0065 [Emerging Sources Citation Index (WoS); Scopus CiteScore: 3.8 (top 93rd percentile)] 9. Olawumi, T.O. & Chan, D.W.M. (2019). Development of a Benchmarking Model for BIM Implementation in Developing Countries. Benchmarking: An International Journal, 26(4), 1210-1232. https://doi.org/10.1108/BIJ-05-2018-0138 [Emerging Sources Citation Index (WoS); Scopus CiteScore: 3.6 (top 81st percentile)] 10. Olawumi, T.O. & Chan, D.W.M. (2019). Building Information Modelling and Project Information Management Framework for Construction Projects. Journal of Civil Engineering and https://doi.org/10.3846/jcem.2019.7841 Management, 25(1), 53-75. [WoS Impact Factor: 2.338; Scopus CiteScore: 4.7 (top 83rd percentile)] 11. Olawumi, T.O., Chan, D.W.M., Wong, J.K.W. & Chan, A.P.C (2018). Barriers to the Integration of BIM and Sustainability Practices in Construction Projects: A Delphi Survey of International Experts. Journal of Building Engineering, 20(November), 60-71. https://doi.org/10.1016/j.jobe.2018.06.017 [WoS Impact Factor: 3.379; Scopus CiteScore: 4.9 (top 97th percentile)] 12. Olawumi, T. O. & Chan, D.W.M. (2018). Identifying and Prioritizing the Benefits of Integrating BIM and Sustainability Practices in Construction Projects: A Delphi Survey of International Experts. Sustainable Cities and Society, 40(July), 16-27. https://doi.org/10.1016/j.scs.2018.03.033 [WoS Impact Factor: 5.268; Scopus CiteScore: 7.5 (top 98th percentile)] 13. Olawumi, T.O. & Chan, D.W.M. (2018). A Scientometric Review of Global Research on Sustainability and Sustainable Development. Journal of Cleaner Production, 183(May), 231-250. https://doi.org/10.1016/j.jclepro.2018.02.162 vii (Top 1% “Highly Cited Paper” in the Field of Engineering – Web of Science Core Collection, 2019 & 2020) [WoS Impact Factor: 7.246; Scopus CiteScore: 10.9 (top 98th percentile)] 14. Ma, X., Xiong, F., Olawumi, T.O., Dong, N. & Chan, A.P.C (2018). Conceptual Framework and Roadmap Approach for Integrating BIM into Lifecycle Project Management. Journal of Management in Engineering, https://doi.org/10.1061/(ASCE)ME.1943-5479.0000647 ASCE, 34(6). [WoS Impact Factor: 3.269; Scopus CiteScore: 6.7 (top 95th percentile)] 15. Olawumi, T.O., Chan, D.W.M. & Wong, J.K.W. (2017). Evolution in the Intellectual Structure of BIM Research: A Bibliometric Analysis. Journal of Civil Engineering and 23(8), Management, https://doi.org/10.3846/13923730.2017.1374301 1060-1081. [WoS Impact Factor: 2.338; Scopus CiteScore: 4.7 (top 83rd percentile)] Refereed Journal Papers (under review) 1. Olawumi, T.O. & Chan, D.W.M. (under review). Green-BIM Assessment Framework for Evaluating Sustainability Performance of Building Projects: A Case of Nigeria. Architectural Engineering and Design Management. Manuscript ID: AEDM-2020-0119 (1st round submitted on 26 August 2020). 2. Olawumi, T.O. & Chan, D.W.M. (under review). Development of a Cloud-based Sustainability Decision Support System (C-SDSS): A Digitalized Automated Green Building Sustainability Assessment Tool. Automation in Construction. Manuscript ID: AUTCON_2019_1409 (1st round submitted on 6 December 2019). 3. Olawumi, T.O. & Chan, D.W.M. (under review). Developing Project Evaluation Models for Smart Sustainable Practices Implementation in the Construction Industry of Nigeria and Hong Kong. Journal of Cleaner Production. Manuscript ID: JCLEPRO-S-20-25203 (1st round submitted on 31 August 2020). viii Refereed Conference Papers (published) 1. Saka, A.B., Chan, D.W.M. & Olawumi, T.O. (2019). A Systematic Literature Review of Building Information Modelling in the Architecture, Engineering and Construction Industry – The Case of Nigeria. In Proceedings of the Environmental Design and Management International Conference 2019 (EDMIC 2019) on Drivers and Dynamics of Change in the Built Environment, 20-22 May 2019, Obafemi Awolowo University, Ile-Ife, Nigeria, 728-738, ISSN 2682-6488. 2. Olawumi, T.O. & Chan, D.W.M. (2018). Critical Success Factors (CSFs) for Amplifying the Integration of BIM and Sustainability Principles in Construction Projects: A Delphi Study. In Proceedings of the RICS COBRA Conference 2018, 23-25 April 2018, London, United Kingdom. ISBN 978-1-78321-274-3, ISSN 2398-8614 (Paper #127 in electronic format). http://bit.ly/39WcqRO 3. Olawumi, T.O. & Chan, D.W.M. (2018). Beneficial Factors of Integrating Building Information Modelling (BIM) and Sustainability Practices in Construction Projects. In Proceedings of the Hong Kong International Conference on Engineering and Applied Science 2018 (HKICEAS 2018), 24-26 January 2018, Hong Kong, 141152, ISBN 978-986-87417-4-4 (in electronic format). Datasets (published) 1. Olawumi, T.O. & Chan, D.W.M. (2019). Building Sustainability Assessment Method (BSAM) - for Countries in sub-Saharan region. Mendeley Data, 1(1). http://dx.doi.org/10.17632/jvjm5h8md3.1 2. Olawumi, T.O. & Chan, D.W.M. (2017). Geospatial map of the global research on sustainability and sustainable development: Generating and converting KML files to map. Mendeley Data, 1(1). https://doi.org/10.17632/sv23pvr252.1 ix Book Chapter (accepted) 1. Olawumi, T.O., Saka, A.B., Chan, D.W.M. & Jayasena, N.S. (2020). Scientometric Review and Analysis: A Case Example of Smart Buildings and Smart Cities. In J.K. Akotia & E. Manu (Eds.) Secondary Research Methods in the Built Environment. Routledge: Taylor & Francis Group Refereed Journal Papers (pending submission) 1. Olawumi, T.O. & Chan, D.W.M. Evaluating the Barriers to the Adoption of Smart Sustainable Practices: A Comparative Analysis between Hong Kong and Nigeria. Building and Environment. 2. Olawumi, T.O. & Chan, D.W.M. Comparative Study of the Benefits of implementing Smart Sustainable Practices between Nigeria and Hong Kong. Sustainable Development. Honors and Awards 1. Merit Award in the Australian Institute of Building (Hong Kong Chapter) Professional Excellence in Building Awards 2019 (Category B3 - Student Achievement). Award Certificate was presented in the AIB(HK) Dinner and Award Night 2019 held at the Sheraton Hong Kong Hotel & Towers on 21 June 2019. Entry Title: BIM-based Sustainability Assessment Model for Buildings: A Choquet Fuzzy Integral Approach. 2. Gold Award in the Chartered Institute of Building (Hong Kong) Outstanding Paper Awards 2018 - (Category - Research Postgraduate Students). Award Certificate and Cash Prize (HK$ 5,000) were presented at the CIOB Hong Kong 45th Anniversary Dinner 2018 held at the New World Millennium Hong Kong Hotel, Tsim Sha Tsui, Kowloon on 21 September 2018. Paper Title: Integrating BIM and Sustainability Practices in Smart Construction Projects: Benefits, Barriers and Drivers – A Delphi Survey of International Experts. 3. Certificate of Outstanding Contribution in Reviewing in recognition of the contributions made to the quality of the journal as a reviewer conferred by the Journal of Cleaner Production published by Elsevier. Awarded in September 2018. x ACKNOWLEDGEMENTS I am very grateful and thankful to several people who have been of enormous help and assistance to me during my PhD study. First and foremost, I am most grateful to the God Almighty, the creator of the heavens and earth for his grace, preservation, strength, inspirations, ability, and knowledge upon my life before and throughout my PhD research project. I wish to express my profound gratitude to my Chief Supervisor, Cr. Dr. Daniel W.M. Chan, Associate Professor in Construction Project Management and Former Associate Head (Teaching), for his continual support and encouragement which served me well during my research study. His continuous guidance and insightful advice have not only added quality and depth to this thesis but also profoundly influenced and shaped me as a scholar. His commitment to my progress is remarkable, he devoted his valuable time and energy in proofreading and advising on each research paper that emanated from this study. More so, I sincerely appreciate him for allowing me the independence to explore ideas for my research while keeping me on right track to achieve the intended outcomes. Meanwhile, I would like to thank my Co-Supervisors, Ir. Professor Albert P.C. Chan and Dr. Johnny K.W. Wong (University of Technology Sydney, Australia) for their precious time and immeasurable support and directions during my research study. More so, I wish to acknowledge the support of the Sustainable City Laboratory under the auspices of the Department of Building and Real Estate (BRE) in funding the initial stages of this PhD research study. I also want to appreciate BRE for also providing financial and material support towards my PhD study via the PhD research studentship. I also appreciate all the academic staff and colleagues in BRE; it was great having an opportunity to interact and learn from each other. I would like to appreciate my research group members – notably, Dr. Nicole Yiu, Mr. Abdullahi Saka, and Ms. Nimesha Jayasena. Also, thanks to the several reviewers and editors that have provided constructive comments and suggestions on the publications that emanated from this study. I am grateful to Dr. Ibukun Famakin, for sharing information about the FCE master scholarship available at the Hong Kong Polytechnic University (PolyU) with me. Although I was successful in my application, but I could not take up the offer for xi some reasons. This later transcends to me getting connected with Dr. Daniel Chan, who intimated me with information about PhD scholarships in Hong Kong; I appreciate this good gesture. I acknowledge the support of senior colleagues in BRE, such as Dr. Boluwatife Abidoye (now of University of New South Wales, Australia), Dr. Gbenga Oladinrin (now of University of Wolverhampton, United Kingdom), and Dr. Robert Osei-Kyei (now of Western Sydney University, Australia) for their advice and assistance during this research study. More so, special thanks go to all industry experts and survey respondents, who participated and devoted efforts in providing valuable opinions and essential data for this research. Also, I sincerely appreciate Arc. Elufioye Oluwabukola, who helped in sourcing the building models for this research study and thanks also go to Qs. Frank Eghenure, Mr. Femi Akinnagbe, Mr. Phillip Ololude, and Mr. Kenny Otasowie for their invaluable help and assistance in the process of data collection. I appreciate the support of Dr. Emmanuel Areo, Dr. Oluwaseyi Awodele, and Dr. Samuel Olatunji (Kabiyesi) of the Federal University of Technology Akure, Nigeria. My stay in Hong Kong has been memorable because of the support and presence of the Nigerian scholar community and families in the city. Thanks to the Association of Nigerian Scholars in Hong Kong (ANSHK), which gave me the privilege and platform to serve firstly as her Information and Relation Officer and later as the President of ANSHK as well as in some of her committees. I appreciate the support and fellowship of Dr. Damilola Akinniyi and his family. My profound gratitude and appreciation to my parents – Dr. Simeon Olawumi and Mrs. Ireade Olawumi for their love, prayers, constant encouragement, and supports throughout my PhD study. Sincere thanks to my siblings – Mr. Isaac Olawumi and Mr. Stephen Olawumi for their support and prayers throughout my PhD study. I owe everything I have achieved to their unfailing love. Special thanks to my in-laws, Mrs. Abosede Abe, and the Abes – Mr. Tobi, Mr. Seun, and Mr. Kehinde Abe for their prayers and encouragement to support my family and me during this period. I sincerely appreciate my Soulmate and lovely wife, Mrs. Taiwo Olawumi, for her sacrifices, perseverance, prayers, and love during my PhD study. Many nights, she stayed awake during the several nights of my “burning of the midnight oil.” To my xii lovely and wonderful son, Master Josiah Oluwadara Olawumi, thanks for your understanding and smiles. xiii TABLE OF CONTENTS CERTIFICATE OF ORIGINALITY............................................................................... i DEDICATION ............................................................................................................. ii ABSTRACT .............................................................................................................. iii LIST OF RESEARCH PUBLICATIONS AND ACHIEVEMENTS ............................. vi ACKNOWLEDGEMENTS ......................................................................................... xi TABLE OF CONTENTS.......................................................................................... xiv LIST OF FIGURES................................................................................................ xxiv LIST OF TABLES ................................................................................................ xxvii CHAPTER 1: INTRODUCTION ................................................................................. 1 1.1 Chapter Overview ............................................................................................. 1 1.2 Background of the Study .................................................................................. 1 1.3 Research Problems and Scope ........................................................................ 7 1.3.1 Benefits of BIM and sustainability practices implementation ................................ 7 1.3.2 Barriers of BIM and sustainability practices implementation ................................ 8 1.3.3 Drivers of BIM and sustainability practices implementation ................................. 9 1.3.4 Why develop a Building Sustainability Assessment Method (BSAM) for Developing Countries in Sub-Saharan Africa? ................................................................. 11 1.3.5 Need for a Cloud-based Sustainability Decision Support System for the BSAM Scheme? ......................................................................................................................... 13 1.3.6 Any necessity for the development of a Green-BIM Assessment Framework for Green Building Projects in Sub-Saharan Africa?.............................................................. 15 1.4 Research Aim and Objectives ........................................................................ 18 1.5 Research Approaches and Contribution to Knowledge .................................. 20 1.5.1 Literature review – extant literature, practice, and experts’ consultations .......... 20 1.5.2 Primary data collection – Questionnaire surveys and Data analysis .................. 21 1.5.3 Case studies – Implementing the models and cloud-based platform ................. 22 1.5.4 Cloud-based system – Coding, database management, and web development 22 1.6 Structure of the Thesis ................................................................................... 23 xiv 1.7 Chapter Summary .......................................................................................... 26 CHAPTER 2: A SCIENTOMETRIC REVIEW OF GLOBAL RESEARCH ON SUSTAINABILITY AND SUSTAINABLE DEVELOPMENT .................................... 28 2.1 Chapter Overview ........................................................................................... 28 2.2 Introduction..................................................................................................... 28 2.2.1 2.3 Research Methodology................................................................................... 31 2.3.1 2.4 Knowledge gap, research objectives, and value ................................................ 30 Literature search strategy and Research data ................................................... 34 Scientometric Analysis, Results and Discussion ............................................ 36 2.4.1 Co-author analysis ............................................................................................ 37 2.4.1.1 Co-Authorship Network .............................................................................. 37 2.4.1.2 Network of institutions/faculties and countries/regions ................................ 40 2.4.2 Co-word analysis ............................................................................................... 42 2.4.2.1 Network of co-occurring keywords .............................................................. 42 2.4.2.2 Network of co-occurring subject categories ................................................ 44 2.4.3 Co-citation analysis ........................................................................................... 46 2.4.3.1 Journal co-citation network ......................................................................... 47 2.4.3.2 Author co-citation network .......................................................................... 50 2.4.3.3 Document co-citation network .................................................................... 52 2.4.4 Clusters analysis ............................................................................................... 55 2.4.4.1 Keywords clusters ...................................................................................... 56 2.4.4.2 Documents co-citation clusters ................................................................... 57 2.4.5 Geospatial analysis ........................................................................................... 59 2.5 Identification of the salient research clusters .................................................. 61 2.6 Conclusions and future directions .................................................................. 63 2.7 Chapter Summary .......................................................................................... 66 CHAPTER 3: EVOLUTION IN THE INTELLECTUAL STRUCTURE OF BIM RESEARCH: A BIBLIOMETRIC ANALYSIS .......................................................... 68 3.1 Chapter Overview ........................................................................................... 68 3.2 Introduction..................................................................................................... 68 3.2.1 Knowledge Gap and Research Objectives and Value........................................ 72 xv 3.3 Research Methodology................................................................................... 74 3.3.1 3.4 Literature Search Strategy ................................................................................. 74 Bibliometric Analysis....................................................................................... 79 3.4.1 Published Research in BIM ..................................................................................... 79 3.4.2 BIM Sub-fields and Research Areas .................................................................. 80 3.4.3 BIM Publications’ Funding Structure .................................................................. 81 3.4.4 Anatomy of BIM Publications ............................................................................. 83 3.5 3.4.4.1 Project Sector Analysis .............................................................................. 83 3.4.4.2 Salient Research Methodologies ................................................................ 83 3.4.4.3 BIM Software and Data Schema ................................................................ 84 Discussions on the BIM Research Categories ............................................... 92 3.5.1 Construction and Project Management (RC6) ................................................... 93 3.5.2 BIM learning, adoption & practice (RC5)............................................................ 94 3.5.3 Building design and Energy conformance (RC2) ............................................... 96 3.5.4 Sustainability (RC9) ........................................................................................... 98 3.6 Research Limitations ...................................................................................... 99 3.7 Conclusions .................................................................................................... 99 3.8 Chapter Summary ........................................................................................ 101 CHAPTER 4: AN EMPIRICAL SURVEY OF THE PERCEIVED BENEFITS OF EXECUTING BIM AND SUSTAINABILITY PRACTICES IN THE BUILT ENVIRONMENT ..................................................................................................... 103 4.1 Chapter Overview ......................................................................................... 103 4.2 Introduction................................................................................................... 103 4.2.1 4.3 The impact of the implementation of BIM and sustainability practices ............. 106 Benefits of BIM and Sustainability Practices Implementation in Construction Projects .................................................................................................................. 109 4.4 Research methodology................................................................................. 112 4.4.1 Statistical tools and reliability test analysis ...................................................... 112 4.4.2 Demographics of the survey participants ......................................................... 113 4.3 Analysis and discussion of survey results .................................................... 115 xvi 4.3.1 Descriptive statistical tests .............................................................................. 115 4.3.2 Inferential statistical tests ................................................................................ 116 4.3.2.1 Statistical tests based on organizational setups ....................................... 117 4.3.2.2 Statistical tests based on professional disciplines..................................... 118 4.3.3 Factor Analysis ................................................................................................ 120 4.3.3.1 Discussions of the clustered benefit factors .............................................. 123 4.4 Conclusions .................................................................................................. 126 4.5 Chapter Summary ........................................................................................ 128 CHAPTER 5: BARRIERS TO THE INTEGRATION OF BIM AND SUSTAINABILITY PRACTICES IN THE BUILT ENVIRONMENT ....................................................... 130 5.1 Chapter Overview ......................................................................................... 130 5.2 Introduction................................................................................................... 130 5.2.1 5.3 BIM and sustainability practices: A review ....................................................... 132 Impediments of implementing smart sustainable practices: A desktop review ... ..................................................................................................................... 134 5.4 Research methodology................................................................................. 138 5.4.1 Statistical tools for data analysis ...................................................................... 139 5.4.2 Respondents' demographics ........................................................................... 141 5.5 Results of statistical analyses ....................................................................... 142 5.5.1 Descriptive statistical tests .............................................................................. 142 5.5.2 Inferential statistical tests ................................................................................ 143 5.5.2.1 Statistical tests based on professional disciplines..................................... 144 5.5.2.2 Statistical tests based on organizational setups ....................................... 144 5.5.3 5.6 Classification of the key barriers based on factor analysis ............................... 147 Discussion of survey findings ....................................................................... 150 5.6.1 Discussion of key cluster factors after factor analysis ........................................... 150 5.6.1.1 Education and knowledge-related barriers ............................................... 151 5.6.1.2 Market-related barriers ............................................................................. 151 5.6.1.3 Organizational and project-related barriers ............................................... 152 5.6.1.4 Information and data-related barriers........................................................ 152 5.6.2 Practical implications of research findings ....................................................... 153 xvii 5.7 Conclusions and recommendations ............................................................. 154 5.8 Chapter Summary ........................................................................................ 156 CHAPTER 6: KEY DRIVERS OF IMPLEMENTING SMART AND SUSTAINABLE PRACTICES IN THE BUILT ENVIRONMENT ....................................................... 158 6.1 Chapter Overview ......................................................................................... 158 6.2 Introduction................................................................................................... 158 6.3 Smart and sustainable practices: Salient issues in the built environment .... 162 6.4 Research Methodology................................................................................. 166 6.4.1 Statistical methods and reliability tests ............................................................ 167 6.4.2 Respondents’ demographics ........................................................................... 168 6.5 Results of statistical analyses ....................................................................... 170 6.5.1 Descriptive statistical tests .............................................................................. 170 6.5.2 Inferential statistical tests ................................................................................ 173 6.5.2.1 Statistical tests based on organizational setups ....................................... 173 6.5.2.2 Statistical tests based on professional disciplines..................................... 173 6.5.3 6.6 Classification of the key drivers based on factor analysis ................................ 174 Discussion of survey findings ....................................................................... 177 6.6.1 Discussion of the clustered KDs ...................................................................... 177 6.6.1.1 Knowledge & industry-related drivers ....................................................... 178 6.6.1.2 Organizational & project-related drivers.................................................... 179 6.6.1.3 Financial, legal & statutory drivers ............................................................ 179 6.6.2 Comparative assessment of the KDs based on respondents’ regions ............. 180 6.6.3 Practical implementation of research findings .................................................. 182 6.7 Conclusions .................................................................................................. 183 6.8 Chapter Summary ........................................................................................ 185 CHAPTER 7: DEVELOPMENT OF A BUILDING SUSTAINABILITY ASSESSMENT METHOD (BSAM) FOR DEVELOPING COUNTRIES IN SUB-SAHARAN AFRICA .. ..................................................................................................................... 187 7.1 Chapter Overview ......................................................................................... 187 7.2 Literature review ........................................................................................... 187 xviii 7.2.1 Establishing the key sustainability assessment criteria .................................... 187 7.2.2 Review of the selected existing green building rating systems......................... 191 7.3 Research methods and data collection......................................................... 193 7.3.1 BSAM scheme: Its documentation and experts’ consultation ........................... 195 7.3.2 Data collection and experts’ demographics ..................................................... 196 7.3.3 Composition of the BSAM scheme - Repository .............................................. 198 7.4 Results and discussion ................................................................................. 199 7.4.1 BSAM Scheme: Analysis of its key sustainability assessment criteria and case study validation .............................................................................................................. 199 7.4.1.1 Key sustainability assessment criteria – score-weight determination and distribution ................................................................................................................. 199 7.4.1.2 Determination of the significance of the sustainability sub-attributes ........ 200 7.4.1.3 Establishing the ratio of each sustainability indicator ................................ 201 7.4.1.4 The BSAM certification grading system .................................................... 203 7.4.1.5 Case study validation for the BSAM scheme ............................................ 204 7.4.2 Comparison of the proposed BSAM scheme with the six selected existing green building rating systems .................................................................................................. 207 7.4.2.1 Allocating credit points to the key sustainability assessment criteria ......... 207 7.4.2.2 Similarities in the radar diagrams for the green building rating tools in comparison to the BSAM scheme .............................................................................. 209 7.4.2.3 Differences in the radar diagrams for the green building rating tools in comparison with the BSAM scheme........................................................................... 210 7.4.2.4 Precedence of the BSAM scheme over the existing green building rating tools ................................................................................................................. 214 7.6 Conclusions .................................................................................................. 216 7.7 Chapter Summary ........................................................................................ 218 CHAPTER 8: APPLICATION OF GENERALIZED CHOQUET FUZZY INTEGRAL METHOD IN THE SUSTAINABILITY RATING OF GREEN BUILDINGS BASED ON THE BSAM SCHEME ............................................................................................ 220 8.1 Chapter Overview ......................................................................................... 220 8.2 Introduction................................................................................................... 220 8.3 State of the art: The General Choquet Fuzzy Integral (GCFI) Method ......... 223 xix 8.3.1 Historical development of the GCFI method .................................................... 223 8.3.2 Underlying conditions for adopting the GCFI method ...................................... 224 8.3.3 Advantages of the GCFI methods over other MCDM techniques ..................... 225 8.4 Application of the GCFI Method: Sustainability Rating of Green Buildings ... 226 8.4.1 Determination of the Building Sustainability Evaluation Index (BSEI) .............. 227 8.4.1.1 Experts’ demographics ............................................................................. 229 8.4.1.2 The fuzzification process .......................................................................... 230 8.4.1.3 The defuzzification process, normalization process and results................ 242 8.4.1.4 Establishing the Building Sustainability Evaluation Index (BSEI) and BSER ... ................................................................................................................. 248 8.4.1.5 8.4.2 Building sustainability grade determination ............................................... 249 Implementation of the BSAM scheme: Case study validation .......................... 250 8.4.2.1 Case study projects descriptions and data ............................................... 250 8.4.2.2 Evaluation of the sustainability performance of the case study projects.... 251 8.4.3 Comparison between the BSAM scheme and other green building rating systems ........................................................................................................................ 254 8.5 Discussion of findings ................................................................................... 255 8.5.1 Weights of the key sustainability criteria .......................................................... 255 8.5.2 Assessment results of the case study projects ................................................ 257 8.6 Conclusions .................................................................................................. 258 8.7 Chapter Summary ........................................................................................ 260 CHAPTER 9: DEVELOPMENT OF A CLOUD-BASED SUSTAINABILITY DECISION SUPPORT SYSTEM (C-SDSS): A DIGITALIZED AUTOMATED GREEN BUILDING SUSTAINABILITY ASSESSMENT TOOL ........................................... 262 9.1 Chapter Overview ......................................................................................... 262 9.2 Development and methodology of C-SDSS platform: Main features and graphical user interfaces (GUI) .............................................................................. 262 9.2.1 C-SDSS main features .................................................................................... 264 9.2.1.1 Coding and optimization process.............................................................. 264 9.2.1.2 Data entry and storage management ....................................................... 264 9.2.1.3 Green building sustainability assessment ................................................. 265 9.2.1.4 Information and documentation ................................................................ 266 9.2.1.5 Output displays ........................................................................................ 266 xx 9.2.2 9.3 C-SDSS graphical user interfaces (GUI) ......................................................... 267 9.2.2.1 Output displays GUI ................................................................................. 267 9.2.2.2 Green building sustainability assessment GUI.......................................... 269 9.2.2.3 Data entry and storage management GUI ................................................ 270 9.2.2.4 Coding and optimization process GUI ...................................................... 272 Validation and Verification of the C-SDSS platform ...................................... 272 9.3.1 Verification of the C-SDSS platform ...................................................................... 273 9.3.2 Case study building projects’ validation exercise ............................................. 273 9.3.2.1 Case study building projects’ descriptions and documentary evidences ... 274 9.3.2.2 Sustainability assessment of the case study projects on the C-SDSS platform ................................................................................................................. 275 9.3.2.3 Comparative sustainability assessment of the case study projects ........... 277 9.3.3 Experts’ validation of the CDSS platform and its integrated BSAM scheme..... 281 9.3.3.1 Validation survey and experts’ demographics........................................... 282 9.3.3.2 Validation survey results........................................................................... 283 9.4 Research implications .................................................................................. 286 9.5. Conclusions .................................................................................................. 288 9.6 Chapter Summary ........................................................................................ 289 CHAPTER 10: GREEN-BIM ASSESSMENT FRAMEWORK FOR EVALUATING SUSTAINABILITY PERFORMANCE OF BUILDING PROJECTS ........................ 291 10.1 Chapter Overview ......................................................................................... 291 10.2 Methodology ................................................................................................. 291 10.2.1 Conceptual Framework Development .............................................................. 291 10.2.1.1 10.2.2 Why conceptual framework for GBA framework development? ................ 293 Experts’ consultation and validation................................................................. 294 10.3 Green-BIM Assessment framework: Development and Process Maps ........ 296 10.3.1 GBA Framework and its information exchange workflow ....................... 297 10.3.1.1 BIM system .................................................................................................. 300 10.3.1.2 Regulatory Documents ................................................................................ 301 10.3.1.3 Data and Evidence ...................................................................................... 302 10.3.1.4 BSAM scheme green building rating system ................................................ 304 10.3.1.5 Relational databases and C-SDSS platform ................................................ 304 xxi 10.4 Experts’ validation surveys ........................................................................... 305 10.4.1 Experts’ demographics .................................................................................... 306 10.4.2 Validation survey results.................................................................................. 307 10.5 Research implications and recommendations .............................................. 310 10.6 Conclusions .................................................................................................. 311 10.7 Chapter Summary ........................................................................................ 312 CHAPTER 11: CONCLUSIONS AND RECOMMENDATIONS ............................. 313 11.1 Chapter Overview ......................................................................................... 313 11.2 Review of the Research Objectives and Conclusions .................................. 313 11.2.1 To identify and assess the inherent benefits, barriers, and critical success factors (drivers) associated with integrating BIM and sustainability principles in building projects. .. ........................................................................................................................ 314 11.2.2 To establish the relative weightings of the key sustainability indicators, sustainability attributes and sub-attributes for buildings. ................................................ 316 11.2.3 To develop a sustainability evaluation index for buildings using the Generalized Choquet Fuzzy Integral method. .................................................................................... 317 11.2.4 To develop a cloud-based sustainability decision support system (C-SDSS) for buildings. ....................................................................................................................... 317 11.2.5 To develop a conceptual Green-BIM assessment framework as a tool for the evaluation of sustainability performance of buildings. .................................................... 318 11.3 Significance and Contributions of the Study ................................................. 319 11.4 Limitations and Recommendations for Future Research Study .................... 322 11.5 Chapter Summary ........................................................................................ 323 REFERENCES ....................................................................................................... 324 APPENDICES ........................................................................................................ 387 Appendix A: The Documentation for the Building Sustainability Assessment Method (BSAM) Scheme ............................................................................................................ 387 Appendix B: Questionnaire Survey Template (International Survey) .............................. 388 Appendix C: Questionnaire Survey Template (Research Objectives #2 – #5)................ 396 Appendix D: Credit points and Significance value of BSAM scheme criteria .................. 400 xxii Appendix E: Cloud-based Sustainability Decision Support System (Graphical User Interfaces - GUI) ............................................................................................................ 408 xxiii LIST OF FIGURES Figure 1.1: BIM usage for Building Sustainability Analysis (Jung & Lee, 2015) ....... 17 Figure 1.2: Map showing the sub-Saharan region of Africa...................................... 19 Figure 1.3: Overall research approach ..................................................................... 23 Figure 2.1: Outline of Research Design ................................................................... 33 Figure 2.2: Literature search and indexing strategy ................................................. 35 Figure 2.3: Distribution of the indexed research corpus from 1991 – 2016 .............. 36 Figure 2.4: Co-authorship network ........................................................................... 39 Figure 2.5: Network of Countries and Institutions ..................................................... 42 Figure 2.6: Network of co-occurring keywords ......................................................... 44 Figure 2.7: Network of co-occurring subject categories............................................ 46 Figure 2.8: Journal co-citation network..................................................................... 50 Figure 2.9: Author co-citation network ...................................................................... 52 Figure 2.10: Document co-citation network .............................................................. 55 Figure 2.11: Keyword clusters network .................................................................... 57 Figure 2.12: Document co-citation clusters .............................................................. 59 Figure 2.13: Geospatial visualization of published research documents (Europe) ... 60 Figure 2.14: Geospatial visualization of published research documents (showing part of China) ............................................................................................................ 61 Figure 3.1: Outline of Research Design ................................................................... 75 Figure 3.2: Literature search & indexing approach ................................................... 78 Figure 3.3: Map network visualization analysis (funding structure) .......................... 82 Figure 4.1: Profile of working experience of survey participants ............................ 114 Figure 5.1: Respondents' demographics ................................................................ 141 Figure 5.2: Ranking results of the factor scale rating for the key cluster factors .... 150 Figure 6.1: Respondents' demographics ................................................................ 169 Figure 7.1: Methodological approach to BSAM scheme development ................... 195 Figure 7.2: Key sustainability indicators (A-H) and their associated attributes ....... 196 Figure 7.3: Score-weight distribution for each of the key sustainability assessment criteria .............................................................................................................. 201 Figure 7.4: Percentage contribution of each of the key sustainability assessment criteria .............................................................................................................. 202 Figure 7.5: BSAM Six-grade certification system ................................................... 203 xxiv Figure 7.6: Comparison of the score-weight distribution of the two case study projects (A & B) in terms of their sustainability indicators ................................ 206 Figure 7.7: Radar diagrams for the green building rating tools based on the credit points of their key sustainability assessment criteria ....................................... 211 Figure 7.8: Comparison chart for the green building rating tools based on the credit points of their key sustainability assessment criteria ....................................... 212 Figure 8.1: Overall framework of the research methodology .................................. 227 Figure 8.2: Hierarchical structure of the sustainability evaluation criteria of the BSAM scheme ............................................................................................................ 228 Figure 8.3: The experts’ demographics .................................................................. 230 Figure 8.4: Representation of the Trapezoidal Fuzzy Numbers ............................. 231 Figure 8.5: BSAM Six-grade certification system: showing the grade levels and corresponding BSER values ............................................................................ 249 Figure 9.1: "Green Projects" GUI ........................................................................... 268 Figure 9.2: "View Project SER" GUI ....................................................................... 268 Figure 9.3: "Compare Projects” GUI ....................................................................... 269 Figure 9.4: Green Building Sustainability Assessment GUIs .................................. 270 Figure 9.5: MySQL relational databases GUI for the C-SDSS platform ................. 271 Figure 9.6: "Project Registration" GUI .................................................................... 271 Figure 9.7: Code reading for the calculation of the fuzzy measures (GCFI algorithm) ........................................................................................................................ 272 Figure 9.8: C-SDSS results of the sustainability analysis of the NB case study projects ............................................................................................................ 276 Figure 9.9: C-SDSS results of the sustainability analysis of the EB case study projects ............................................................................................................ 277 Figure 9.10: Comparative assessment of the NB and EB cases study projects respectively on the C-SDSS platform .............................................................. 280 Figure 10.1: Components of the proposed GBA tool .............................................. 298 Figure 10.2: Data required by each GBA framework’s component at the building development stages ........................................................................................ 299 Figure 10.3: Process map for the BIM system of the GBA framework ................... 301 Figure 10.4: Process map for the Regulatory documents of the GBA framework .. 302 Figure 10.5: Process map for the Data and Evidence component ......................... 303 Figure 10.6: The C-SDSS Project Assessment Interface ....................................... 305 xxv Figure A1: Cover page of the BSAM scheme documentation ................................ 387 Figure E11: "Delete Projects" GUI.......................................................................... 408 Figure E12: Sign-in GUI ......................................................................................... 408 Figure E13: User registration GUI .......................................................................... 409 Figure E21: Programming codes for the "Compare Projects" GUI ......................... 410 Figure E22: HTML codes for the "Delete Project(s)" GUI ....................................... 410 Figure E31: Comparative assessment of the NB projects respectively on the CSDSS platform – based on the sustainability attributes ................................... 411 Figure E32: Comparative assessment of the EB projects respectively on the C-SDSS platform – based on the sustainability attributes .............................................. 412 xxvi LIST OF TABLES Table 2.1: Top 13 most productive authors with their h-index .................................. 37 Table 2.2: Countries' citation burst ........................................................................... 41 Table 2.3: Keywords' citation bursts ......................................................................... 43 Table 2.4: Top 20 source journals in the research corpus ........................................ 48 Table 2.5: Journals' citation bursts ........................................................................... 49 Table 2.6: Top 35 cited articles based on WoS citation metric ................................. 53 Table 2.7: Keyword clustering of Sustainability research (1991 - 2016) ................... 56 Table 2.8: Documents co-citation clusters of Sustainability research (1991 - 2016) 58 Table 3.1: Summary of Recent BIM Literature Reviews ........................................... 76 Table 3.2: Comparison between previous BIM Literature Reviews' Findings and the Current study ..................................................................................................... 77 Table 3.3: Indexed Corpus Profile ............................................................................ 78 Table 3.4: BIM journal publication lists and Impact factors....................................... 86 Table 3.5: BIM Research Categories ....................................................................... 88 Table 3.6: Theme loading for the BIM research areas ............................................. 89 Table 3.7: Funding structure .................................................................................... 91 Table 3.8: Articles' project sector analysis ............................................................... 91 Table 3.9: BIM Software and data schema .............................................................. 92 Table 4.1: Summary of identified benefits of the implementation of BIM and sustainability practices ..................................................................................... 108 Table 4.2: Benefits of implementing BIM and sustainability practices: inter-group comparisons .................................................................................................... 119 Table 4.3: Post-hoc Tukey test results for the respondents' groups ....................... 120 Table 4.4: Factor structure of the varimax rotation on the benefit's' factors ........... 122 Table 4.5: Ranking results of the factor scale rating for the benefit clusters .......... 123 Table 5.1: Summary of identified barriers to the implementation of smart sustainable practices .......................................................................................................... 137 Table 5.2: Post-hoc Tukey test for the organizational setups ................................. 145 Table 5.3: Barriers to smart sustainable practices in the built environment: intergroup comparisons .......................................................................................... 146 Table 5.4: Factor structure of the varimax rotation on the key barrier factors ........ 148 xxvii Table 6.1: Summary of identified KDs for the execution of smart sustainable practices .......................................................................................................... 165 Table 6.2: Key drivers for the execution of smart sustainable practices in the construction industry: inter-group comparisons ............................................... 172 Table 6.3: Factor structure for the PCA analysis of the KDs .................................. 176 Table 6.4: Ranking results of the factor scale rating for the KDs clusters .............. 178 Table 6.5: Comparative assessment of the KDs based on the respondents’ regions ........................................................................................................................ 180 Table 7.1: Review of relevant sources for the development of the BSAM scheme 188 Table 7.2: BSAM scheme and the six selected green building rating systems....... 193 Table 7.3: The experts' demographics ................................................................... 197 Table 7.4: Weighting average for the sustainability indicators and the overall sustainability performance (Sp) values for the case study projects .................. 206 Table 7.5: Distribution of the credit points for key sustainability assessment criteria of BSAM scheme and the six selected green building rating tools ...................... 208 Table 7.6: Allocation of the credit points for the eight key sustainability assessment criteria (A - H) for each of the green rating tools .............................................. 210 Table 8.1: Relationship between the TFN and degree of importance (linguistics variables) on a five-linguistic-term scale .......................................................... 232 Table 8.2: Average values of the degree of significance and tolerance zones of each criterion (SA) using TFN .................................................................................. 234 Table 8.3: Evaluation result of the GCFI algorithm for the fuzzy valence function, 𝒇𝒊 ∝ for ∝ = 0 ................................................................................................... 237 Table 8.4: Evaluation result of the GCFI algorithm for the fuzzy valence function, 𝒇𝒊 ∝ for ∝ = 1 ................................................................................................... 238 Table 8.5: For ∝ = 0, fuzzy measures gA(i) and 𝝀 values ...................................... 241 Table 8.6: For ∝ = 1, fuzzy measures gA(i) and 𝝀 values ...................................... 242 Table 8.7: Defuzzification and normalization results for the sustainability criteria (SI & SA) using GCFI method................................................................................... 246 Table 8.8: Building Sustainability Evaluation Index (BSEI) – for the two New Buildings Case Studies .................................................................................... 252 Table 8.9: Building Sustainability Evaluation Index (BSEI) – for the two Existing Buildings Case Studies .................................................................................... 253 xxviii Table 8.10: Precedence of the BSAM scheme over the existing green building rating systems ........................................................................................................... 254 Table 9.1: Descriptions of the four-case study building projects ............................ 274 Table 9.2: Experts' demographics for validation survey ......................................... 283 Table 9.3: Validation survey results of the C-SDSS platform and the BSAM scheme ........................................................................................................................ 285 Table 10.1: Descriptions of the data/information required for the GBA tool............ 298 Table 10.2: Demographics of the experts involved in the validation process ......... 307 Table 10.3: Validation results of the GBA framework ............................................. 309 Appendix D1: Determination of credit points (score-weights) for the BSAM scheme sustainability criteria ........................................................................................ 400 Appendix D2: Classification of the significance of each sustainability sub-attribute of the BSAM scheme ........................................................................................... 404 Appendix D3: Weight average for the sustainability attributes for the case study projects ............................................................................................................ 407 xxix CHAPTER 1: INTRODUCTION1 1.1 Chapter Overview This chapter presents and discusses the background of the research, states the scope of the study and the research problems that give rise to each of the research objectives, states the research aim and objectives. It also illustrates the various research approaches adopted and the contribution of the study. Also, this chapter presents the structure of the thesis to ease navigation through the chapters 1.2 Background of the Study Green Building Information Modelling (GBIM) is an emerging trend in the built environment which is described by Wu and Issa (2014b) as a synergy between Building Information Modelling (BIM) and sustainability goals. It is a “symbiotic convergence of the two separate trends into an emerging practice” (McGraw-Hill Construction, 2013). The implementation of BIM in construction projects was categorized into two forms: (1) BIM products or technology; and (2) BIM process (Olawumi et al., 2018; Olawumi & Chan, 2019b); while sustainability is often defined This chapter is largely based upon the following published and working papers: Olawumi, T.O., & Chan, D.W.M. (2018d). Identifying and prioritizing the benefits of integrating BIM and sustainability practices in construction projects: A Delphi survey of international experts. Sustainable Cities and Society, 40, 16–27. https://doi.org/10.1016/j.scs.2018.03.033 Olawumi, T.O., & Chan, D.W.M. (2019a). An empirical survey of the perceived benefits of executing BIM and sustainability practices in the built environment. Construction Innovation: Information, Process, Management, 19(3), 321–342. https://doi.org/10.1108/CI-08-2018-0065 Olawumi, T.O., & Chan, D.W.M. (2020b). Concomitant Impediments to the implementation of Smart Sustainable Practices in the Built Environment. Sustainable Production and Consumption, 21(January), 239-251. https://doi.org/10.1016/j.spc.2019.09.001 Olawumi, T.O., Chan, D.W.M., Wong, J.K.W., & Chan, A P.C. (2018). Barriers to the Integration of BIM and Sustainability Practices in Construction Projects: A Delphi Survey of International Experts. Journal of Building Engineering, 20, 60–71. https://doi.org/doi.org/10.1016/j.jobe.2018.06.017 Olawumi, T.O., & Chan, D.W.M. (2020c). Key Drivers for Smart and Sustainable Practices in the Built Environment. Engineering, Construction, and Architectural Management, 27(6), 1257–1281. https://doi.org/10.1108/ECAM-06-2019-0305 Olawumi, T.O., Chan, D.W.M., Chan, A.P.C. & Wong, J.K.W. (2020). Development of a Building Sustainability Assessment Method (BSAM) for Developing Countries in sub-Saharan Africa. Journal of Cleaner Production, 263(August), Article 121514. https://doi.org/10.1016/j.jclepro.2020.121514 Olawumi, T.O., & Chan, D.W.M. Development of a Cloud-based Sustainability Decision Support System (C-SDSS): A Digitalized Automated Green Building Sustainability Assessment Tool. Olawumi, T.O., & Chan, D.W.M. Green-BIM Assessment Framework for Evaluating Sustainability Performance of Building Projects: A Case of Nigeria. Olawumi, T.O., & Chan, D.W.M. (2019d). Critical Success Factors of Implementing Building Information Modelling (BIM) and Sustainability Practices in Construction Projects: A Delphi Survey. Sustainable Development, 27(4), 587–602. https://doi.org/10.1002/sd.1925 1 1 on the basis of the holistic fulfillment of its three fundamental pillars; which are the environmental, social and economic sustainability (Olawumi & Chan, 2018a; Wong & Kuan, 2014). Moreover, to facilitate and ensure construction firms and the industry integrate green BIM into the projects, there must be a strategic plan and guidelines for its implementation (Wu & Issa, 2014b). In recent years, the construction industry has been trying to adopt smart tools which are based on information and communication technologies (ICT) such as BIM, virtual reality, augmented reality systems, and cloud technologies among others to aid construction process and facilitate the integration of other domain knowledge like sustainability (Adamus, 2013), project management (Ahankoob et al., 2018; Ajam et al., 2010), cost control (Ahn et al., 2016), safety management (Zhang et al., 2015), etc. Bibri and Krogstie (2017) argued the importance of these smart tools to enhance and support theoretical concepts such as sustainability which according to Olawumi and Chan (2018a) has been gaining immense interest from academics, industry professional, and the government. The definition and concept of sustainability has been discussed in previous studies (Olawumi & Chan, 2018a; WCED, 1987; Wong & Zhou, 2015); while the concept of smart tools and buildings have been defined and discussed in Cugurullo (2017). Antón and Díaz (2014) noted that the construction industry has started to embrace the concept of BIM and sustainability and suggested that the concepts should be implemented early into the project as it provides the best opportunity to impact the project effectively. The need to integrate BIM and sustainability in a project was stressed by Sun et al. (2016) who noted that for a building to be energy-sufficient, enabling software is needed to simulate and predict the energy performance. Azhar et al. (2011) pointed out that there is an increasing demand for green buildings due to the minimal environmental impact of such buildings and its relatively low lifecycle costs. However, despite the hypothetical and some few real-life evidence-based project benefits of adopting BIM and sustainability practices, Wu and Issa (2014b) observed that the potential of green BIM is yet to be explored in most construction projects, and Olawumi et al. (2017) revealed the inadequacy or lack of relevant standards on BIM and sustainability in most countries. More so, Wu and Issa (2014b) and McGraw-Hill Construction (2010) observed stakeholders’ inclinations for the adoption of green BIM had focused mostly on the 2 technological-aspect and less on other areas such as the business process of implementing BIM and even the sustainability aspect of green BIM. Also, Cugurullo (2017) highlighted the differences and challenges in the built environment in the quest to decipher between eco-cities and smart cities and the appropriate approach to achieve the initiative. Some key attributes of BIM that can be exploited for sustainability issues to enhance smart and sustainable practices in construction projects based on the extant literature includes: 1) As a decision-making tool (Hope & Alwan, 2012); 2) Energy simulation or daylighting simulation (Olawumi et al., 2017); 3) Evaluation of the embodied CO2 over the lifecycle of a building (Capper et al., 2012); 4) Validation of compliance with sustainability criteria (Sheth et al., 2010). Others include – 5) Storage of big data of building information that can be extracted to rating using any available green building rating systems like LEED or BREEAM (Hope & Alwan, 2012); 6) Visualization and walkthroughs for project teams especially as it relates to energy systems in buildings (Olawumi et al., 2017; Sheth et al., 2010); and 7) Improved communication and coordination of construction processes from planning to commissioning (Olawumi & Chan, 2019e). Furthermore, a review of these smart tools shows that BIM has found more use and received the most widespread implementation in the built environment (Bradley et al., 2016; Jung & Lee, 2015; Ma et al., 2018). Hence, this study will focus solely on BIM as a smart tool to aid the sustainability of the built environment, with peculiar emphasis on the construction sector. The process of integrating these smart tools such as BIM to facilitate the implementation of sustainability practices is regarded as smart sustainable practices in this study. Olawumi et al. (2017) highlighted some BIM tools, processes, and software that has found applications in the building design analysis and simulation towards aiding the relevant stakeholders to make sound sustainability-related decisions. Another application of BIM to aid sustainability practices in the literature includes – the use of plugins in BIM software to assess some sustainability parameters in buildings by (Oti et al., 2016). Also, Tah and Abanda (2011) utilize semantic web tools to evaluate the sustainability performance of projects and energy simulation. However, despite the robustness of BIM, its interoperability and proprietary issues have limited its application to sustainability issues (Olawumi et al., 2017). 3 The main idea driving the concepts of a sustainable smart city in the construction industry is primarily the development of standards and the implementation of BIM and sustainable practices. Several research studies have discussed the possibilities of BIM to advance the implementation of sustainability practices in construction projects. Alsayyar and Jrade (2015) developed an innovative model which integrates BIM tools with sustainable design requirements to evaluate the cost and benefits of a proposed building in the planning and design stages. The model was developed with a database module and tested on a real-life project. Moreover, Gilkinson et al. (2015) regard BIM as a revolutionary design-based technology and process which provides considerable value to construction projects throughout the lifecycle stages (Autodesk, 2011). BIM implementation can be considered from two aspects- (1) the use of 3D technology (software) to model and analyze building model using software such as Revit, ArchiCAD etc. (2) the process/conceptualization which enable other knowledge domain such as cost, schedule, project management, safety, sustainability parameters to be embedded in BIM software to provide one-source, central hub of information for project stakeholders. Olatunji et al. (2017b) and Olawumi and Chan (2018e) affirms BIM capability to offer both functions (application and process) which enables it to be useful for construction stakeholders and organizations in managing project data. The integration of BIM and sustainability practices implies leveraging on BIM technologies such as software and plugins, cloud platforms to facilitate sustainability assessment of infrastructural and construction projects (Olawumi & Chan, 2018d). However, there have not been a uniform adoption and implementation of BIM initiatives and sustainability in most countries, with the United States and the United Kingdom, the leading nations in its adoption (Olawumi et al., 2017), likewise for sustainability. The five-dimensional (5-D) BIM which incorporates cost data can assist to avoid cost overrun on construction projects and facilitate substantial returns on investment for the client (Olatunji et al., 2017b). The next phase of nD BIM is the 6D BIM which attempt to utilize BIM to address issues such as sustainability in construction projects which is consistent with the views of Bradley et al. (2016), who stressed the capacity of BIM to expand into domains such as sustainable/green buildings of which BIM was not originally 4 conceived to address. Sustainability is a sophisticated theme in the construction field which involves a balanced play between the social, economic and environmental pillars of sustainable development (Olawumi & Chan, 2018a). In recent years, several infrastructural projects have sprung up in urban cities across the world promoting the ideas of sustainable built environment tagged with names such as ‘smart cities’ or ‘eco-cities’ (Cugurullo, 2017). Also, several approaches have been suggested by advocates of sustainable smart cities toward ensuring the execution of construction projects with these ideas in mind. However, according to Batty (2012), Bettencourt and West (2010), the standards of these advocates of these projects for smart, sustainable cities are unclear, undefined, and often chaotic. Hence, making the drive and concept of city-making to achieve sustainability impossible. Moreover, some cities have demonstrated possibilities in adopting smart technologies in its infrastructural development to emerge as smart cities such as Hong Kong (Cugurullo, 2017), Milano (Milano Smart City, 2017), Barcelona (Barcelona City Council, 2017) and Vienna (Smart City Wien, 2017). Also, a portrayed example of an eco-city is Masdar City envisioned as a greenprint for innovative sustainable development and a city for the future (Masdar Initiative, 2017). Although, Masdar City is often promoted as the world’s most sustainable city (Cugurullo, 2017). However, its failure makes the most use of smart technologies has weakened its ability to resolve some issues related to energy, water supply chain management and ecological impact of the settlement (Crot, 2013; Cugurullo, 2013). The above example of an eco-city (Masdar City) further strengthens the stand of this paper for a cohesive implementation of smart technologies (such as BIM) and sustainability practices in the construction industry. For a smart city such as Hong Kong, Cugurullo (2017) argued that the smart interventions in the city are insensitive to the ideals of sustainable development with resulting environmental pollution and other urban problems. More so, Hong Kong adopted a project-based approach to smart urbanism rather than a whole system; leading to a fragmented system of different entities (Cugurullo, 2017). Hence, it is required for cohesive and strategic planning to synergy different fragmented smart projects and also integrates sustainability practices to achieve a smart, sustainable city. 5 There are some sustainability assessment techniques or building rating systems (such as LEED, BREEAM, etc.) that have been developed to evaluate who well a building project meet some defined criteria for such infrastructure to be considered green or sustainable. Moreover, Sala et al. (2015) highlighted some inadequacy in some of these techniques will make them unreliable and inconsistent and much of the issues are linked to the fuzziness of the sustainability concept itself. Nevertheless, there have been some application of smart technologies and sustainability practices in some projects such as BIM for sustainable material decisions (Ahmadian et al., 2017); BIM for sustainable design (Wong & Fan, 2013), GIS-based facility management (Kang & Hong, 2015); BIM-based energy analysis (Gourlis & Kovacic, 2017). A comprehensive review of the body of literature was examined by Olawumi and Chan (2018a). Yusof et al. (2016) examined the influence of project’s stakeholders’ behavior on the implementation of sustainability practices which reveal a positive correlation between the firm’s management practices in respect of energy efficiency and waste and the implementation of sustainability ideals during project execution. Since construction projects are people-driven, it is expected the project stakeholders are well-informed on the ideals of sustainable development. More so, Eurostat (2013) reported that 859 million tons of waste were generated from construction activities in the European Union; also, Fuertes et al. (2013) regards the construction industry as a significant source of water, noise, and air pollution. In countries such as China and Malaysia, construction-related activities account for 45-46% of the overall energy consumption (MIGHT, 2014; Zhaojian & Yi, 2006); along with about 30% of solid waste in China (Lu & Tam, 2013); and 30% of greenhouse gas emissions in Malaysia (MIGHT, 2014). These case studies reveal the immense potential for the construction industry to embrace the ideals of sustainability (Birkeland, 2014) as well as its cohesive implementation with smart technologies. Therefore, since the construction activities involved several stakeholders such as the clients, architects, project managers, engineers among others (Olawumi & Ayegun, 2016); it is necessary for the stakeholders to well experienced in the use and implementation of smart tools such as BIM and adhere to the ideals of sustainability (Mom et al., 2014a). 6 1.3 Research Problems and Scope This section discusses the knowledge and practice gaps that gives rise to the current study’s aim and objectives. More so, the scope of the study is discussed within the subsections 1.3.1 – 1.3.6. 1.3.1 Benefits of BIM and sustainability practices implementation Studies by De Boeck et al. (2015), and Chandel et al. (2016) highlighted significant research gap in research and practice on the utilization of innovative tools like BIM in sustainability practices. Accordingly, they noted that much emphasis is being placed on the analysis and optimization of energy performance on residential buildings (Chandel et al., 2016; De Boeck et al., 2015) and less on other building typologies such as commercial and industrial buildings (Ruparathna et al., 2016). Also, Abanda and Byers (2016) examined the practical use of BIM in the simulation of energy performance. Moreover, it is necessary to point out that ‘energy performance’ of buildings is a subset of the environmental aspect of sustainable development and green buildings; and according to Ahmad and Thaheem (2017) to achieve sustainable smart cities initiative and green buildings, there must be a balanced play between the economic, social and environmental pillars of sustainability. Moreover, recent studies (see Hosseini et al., 2016; Mao et al., 2016) revealed that inadequate knowledge of the benefits of these concepts had hindered its implementation in the construction industry. Meanwhile, studies such as Mom et al. (2014b, 2014a) have examined some benefits and drivers of BIM adoption in Taiwan. However, these studies focused solely on BIM. Previous studies (see Abdirad, 2016; Ahmad & Thaheem, 2017; Antón & Díaz, 2014; Azhar, 2011) which employed BIM for sustainable construction practices have been limited by their scope. Some of the authors either focused on a subcategory item of sustainability such as energy or LCA, other studies were defined by being confined to a country or building typology. Although some of the benefits identified by previous authors might apply to a single application of either BIM or sustainability practices in construction projects; the study aims to fill the gap by identifying the key benefits that are obtainable when both concepts are adopted in a project as well as categorize them based on the measures of assessment- either qualitative/quantitative or both. 7 1.3.2 Barriers of BIM and sustainability practices implementation Much criticism has been raised about the sole implementation of either smart initiative or sustainability practices in the built environment (Cugurullo, 2017) due to the difficulties and more problems caused by its adoption. Hence, Olawumi et al. (2018) advocated for the implementation of concepts of sustainable smart practices to facilitate a holistic sustainability development of the built environment. Meanwhile, there is still vagueness regarding what constitutes smart- and eco-initiatives (Angelidou, 2015; Olawumi & Chan, 2018b). Extant literature (see Olawumi et al., 2017; Olawumi & Chan, 2017, 2018a; Wong et al., 2014) have conducted reviews on the concepts of smart sustainable practices as it applies to both industry practice and teaching. Also, previous studies (Kivits & Furneaux, 2013; Olatunji et al., 2016b, 2017b) illustrated several attempts by the construction industry to utilize BIM to implement sustainability practices in building projects. However, issues related to inadequate coordination in organization and collaboration among key stakeholders has been a bane of the built environment. Adamus (2013) and Ma et al. (2018) accentuated that a critical challenge with implementing sustainable practices in the industry is the need to balance the implementation of the three pillars of sustainable development (social, environmental and economic sustainability) in projects. More so, where there has been an implementation of sustainability practices in building projects, more emphasis has been on the environmental sustainability construct (Ali & Al Nsairat, 2009; Berardi, 2012). More so, studies such as Chandel et al. (2016) and De Boeck et al. (2015) pointed out that there are still significant gaps in practice in the adoption of innovative tools such as BIM for the implementation of sustainability practices in the construction industry. Studies such as Hosseini et al. (2015) and Mao et al. (2016) emphasized that without sufficient knowledge on the status (such as its barriers etc.) of the implementation of these concepts in the construction industry; it would be difficult to improve track aspects of its implementation that is still lagging. Olawumi and Chan (2018a) highlighted some current application of BIM in implementing sustainability in building projects. 8 Apart from these, there are several smart technologies and tools employed in the construction industry which include: (i) Building Information Modelling (BIM); (ii) virtual reality; (iii) semantic web technology or ontology; (iv) augmented reality; (v) sensors; (vi) Radio-frequency identification (RFID); and (vii) Point-cloud data extraction, among others (Olawumi et al., 2017). However, the current study will discuss one of these smart tools- BIM and the challenges of utilizing it to enable the implementation of sustainable practices in the built environment. Although the other smart devices are being used in the construction industry, BIM is still the most widely employed smart tool (Jung & Lee, 2015; Olawumi et al., 2017; Wong & Zhou, 2015). Virtually every project stakeholder can also utilize BIM, and it is also a multifunctional technology. Although some previous research studies have highlighted the profound barriers relating to BIM in the construction industry – none is yet to appraise the impediments militating against adopting both BIM and sustainability practices on the same building project. Accordingly, this study reviewed the existing literature to gather solid evidence of the challenges faced by the built environment in the implementation of sustainable smart practices. 1.3.3 Drivers of BIM and sustainability practices implementation Meanwhile, a plethora of published literature (see Ali & Al Nsairat, 2009; Anthopoulos, 2017; Ilhan & Yaman, 2016; Olawumi & Chan, 2018d; Shi et al., 2013) have provided holistic reviews and undertook empirical studies to discuss and shows the different application and the use of smart tools to aid sustainability issues. However, no study has examined the drivers of implementing smart and sustainable practices across regions as undertaken in this study. Also, as seen in the previous sections and the extant literature (Jung & Lee, 2015; Malleson, 2012; Olawumi & Chan, 2018a) among others; there has been a varied adoption, implementation, and application of BIM and sustainability practices in the construction industry. The construction industry is given more focus in this study as part of the built environment because according to previous studies (Abanda & Byers, 2016; Bynum et al., 2013), buildings account for one-third of the global energy use and one-fifth of the greenhouse gases emission. 9 Furthermore, according to Gourlis and Kovacic (2017) and Olawumi and Chan (2020), BIM has offered encouraging promises to optimize energy consumption and reduce the carbon footprints of the building facilities. Therefore, the scope of the study is delimited to construction projects (as a subset of the built environment), BIM (a type of smart tools), and sustainability practices as it relates to the whole lifecycle of buildings. Hence, this study aims to take deeper insights by investigating and assessing the key drivers (KDs) that aid the implementation of smart and sustainable practices in the construction industry and projects. Several attempts have been made in the extant literature to address issues related to smart and sustainable practices in the construction industry. For instance, a study by Abanda and Byers (2016) utilized BIM tools to simulate the energy performance of buildings. Although the findings are of significant value, but the focus on the ‘energy’ criterion limits its ability to influence building sustainability. Similar studies by Tsai et al. (2014b) and Oti et al. (2016) demonstrated the use of BIM plugins to embed some sustainability criteria to assess the greenness of building projects. However, these studies place emphasis on a single construct of sustainable development and fail to provide ways to enhance its adoption and implementation. Meanwhile, a few studies have attempted to investigate the drivers to BIM adoption, such as Tsai et al. (2014a) and Chan et al. (2019a) who examine BIM adoption in Taiwan and Hong Kong, respectively. These studies fail to consider how BIM can help improve sustainability practices in the construction industry. Similar studies by Olawumi and Chan (2018d, 2020) have examined the benefits and barriers to the implementation of BIM and sustainability practices; hence, there is a salient need to examine the critical success factors that can drive its implementation. Furthermore, a review by previous studies (see Jung & Lee, 2015; Olawumi et al., 2017; Olawumi & Chan, 2018a) reviewed an uneven rate of adoption of BIM and sustainability across the various regions in the world. These relevant knowledge gaps in the extant literature and practice will be bridged and addressed in this study. Also, the need to enhance the sustainability potential of the built environment and building projects as outlined in the sustainable development goals of the United Nations motivates and necessitates this study. The current study reiterated the need for the application of BIM and sustainability 10 practices in construction projects as against the singularity of the adoption of either BIM or sustainability practices initiatives. 1.3.4 Why develop a Building Sustainability Assessment Method (BSAM) for Developing Countries in Sub-Saharan Africa? A desktop review of the extant literature conducted by Olawumi and Chan (2018a) and Olawumi and Chan (2017) reveals some salient sustainable development issues in the built environment. The statistics of the construction industry regarding its energy consumption rate stand at 32% of the global consumption rate, its carbon emissions stand at 40%, it contributes about 40% of the global solid waste generation; utilizes 12% of the global freshwater and 1/3 of the global material usage (IPCC, 2007; Johansson et al., 2012; Olawumi & Chan, 2019a; UNEP, 2011; Ürge-Vorsatz et al., 2007; WEC, 2013). According to Tam et al. (2019b) and Le et al. (2018), the increase in these carbon emissions is a major contributory factor to global warming as well as the increased energy consumption due to the development of new houses (Gobbi et al., 2016). There is an increasing focus and attention on sustainability issues in the built environment which has led to an increase in the number of certified green buildings nowadays when compared to the advent of the Building Research Establishment Environmental Assessment Method (BREEAM) in the year 1990. Green buildings have been recognized as the flagship of sustainable development in recent years with the increasing responsibility to cater to and balance the social, economic, and environmental sustainability issues (Ando et al., 2005). Green building rating systems (GBRS) have provided an effective means to assess the sustainability performance of various construction projects – be it buildings, civil engineering works, or infrastructure, as well as the integration of sustainable development objectives in such projects (Ali & Al Nsairat, 2009). Currently, there are over 400 registered software tools to assess various aspects of sustainability in buildings (Nguyen & Altan, 2011). More so, there are several green building rating systems such as BREEAM, Leadership in Energy and Environmental Design (LEED), Comprehensive Assessment System for Built Environment Efficiency (CASBEE), Green Star, BEAM Plus, Green Mark, among others, already in place worldwide. These green rating systems are used to address the quality of the building performance throughout its lifecycle as well as the impact of building on its surrounding ecosystem. 11 Ali and Al Nsairat (2009) argued that the use of these green rating systems in the evaluation of sustainability performance could yield significant benefits that might not be obtainable through the standard practice in the construction industry. A review of four Malaysian green rating tools by Hamid et al. (2014) revealed that these tools place emphasis on environmental sustainability and accordingly recommended the merging of these tools to better handle sustainability issues across the building lifecycle stages. Leading green rating tools such as LEED have similar disadvantages (Ismaeel, 2019; Wu et al., 2016). These figures further highlight the significance of sustainable buildings which are needed to improve the quality of life and health of its occupants, increase productivity, reduce air pollutions and CO 2 emissions, and enhance the efficiency of energy equipment among others. However, a review of the extant literature and existing green building rating tools reveals some significant gaps in the existing body of knowledge. For instance, there are currently no available green rating systems that are suited for the local context of developing countries in sub-Saharan Africa. Although, some existing green rating tools such as BREEAM, LEED, and Green Star as discussed in Chapter 7 have been attempting to expand their respective rating tools beyond the borders of the originating countries (Ali & Al Nsairat, 2009; Banani et al., 2013; Berardi, 2012; Illankoon et al., 2017; Mahmoud et al., 2019). However, none of these existing rating tools have extended their reach and rating tool to suit the local context of countries in the subSaharan region. Meanwhile, as argued by Todd and Geissler (1999) and Banani et al. (2013), the regional and local context of GBRS has a significant effect on the importance and priority given to each sustainability criterion in each rating system. A study by Hamid et al. (2014) argued the need to ensure that national and international green rating tools are tailored to the local context to drive green building forward. Hence, as reported in the extant literature (Alyami & Rezgui, 2012; Xiaoping et al., 2009), these regional variations in the priority of the key sustainable criteria hinder the direct use of the rating tool beyond the country of its origin or the local context to which it was designed for use. Moreover, these existing GBRS place more substantial considerations on the environmental sustainability issues with little account or a total neglect of social and economic pillars of sustainable development (Ding et al., 2018; Illankoon et al., 2017; 12 Nguyen & Altan, 2011; Olawumi et al., 2018; Olawumi & Chan, 2018d). Also, the Green Mark rating system does not allocate credit points for the ‘transportation’ criterion (BCA, 2015). Hence, to provide a better evaluation of the ‘greenness’ or sustainability of buildings; Alwisy et al. (2018); Illankoon et al. (2017) recommended that future development of green rating tools should consider all three sustainability pillars. Given the above, this study aims to develop a sustainability assessment tool for buildings (both new and existing buildings) to suit the local context of the sub-Saharan region as well as to establish the importance of the key sustainability criteria through their score-weighted category. The proposed Building Sustainability Assessment Method (BSAM) scheme covers the triple pillars of sustainable development and provides profound improvements to the existing green building rating systems. 1.3.5 Need for a Cloud-based Sustainability Decision Support System for the BSAM Scheme? In recent years, there has been increased large-scale development and interventions in the built environment in Africa and around the world due to the urgency to shore up the gap in the housing deficits. Accordingly, Du Plessis (2007) argued that such interventions, especially in developing countries, must be socially and economicallycentric and not just based on environmental factors alone. More so, Du Plessis (2007) outlined and suggested a number of enablers to facilitate the implementation of sustainability in developing countries, especially in Africa – one of which is the adoption and implementation of sustainable construction technologies and practices. A key challenge to implementing these suggestions according to Du Plessis (2007) and Banani et al. (2013), is the development of regional or local context strategies and technologies for its practice. More so, a review of the adoption of sustainability across regions and countries in the extant literature (Jung & Lee, 2015; Olawumi & Chan, 2017, 2018a) revealed a slow adoption and implementation of sustainability and green buildings in Africa. Although, there have been a number of multi-million dollar infrastructural and building projects development across Africa; it is difficult to verify the sustainability credentials or performance of these building projects due to a lack of a local or regional-based green building rating system which suit the context of the regions in Africa or of a particular country (Ding et al., 2018; Mahmoud et al., 2019; Olawumi & 13 Chan, 2018d). Also, the existing green rating tools such as the Building Research Establishment Environmental Assessment Method (BREEAM), Leadership in Energy and Environmental Design (LEED), Comprehensive Assessment System for Built Environment Efficiency (CASBEE), Green Star, BEAM Plus, Green Mark, among others places more emphasis and considerations on the environmental sustainability criteria (BCA, 2010; BRE, 2018; HKGBC, 2019; Mahmoud, 2017; USGBC, 2017). Hence, these existing green rating tools, according to Ali and Al Nsairat (2009) and Ahmad and Thaheem (2017), are not suited to Africa’s developing countries, which places more emphasis on the social and economic sustainability criteria. The social and economic sustainability (otherwise captioned as ‘people’ and ‘profit’ respectively) are related to the standard of living which is generally between very low and medium for African countries compared to other developed regions. According to the Human Development Index (HDI) of the United Nations (http://hdr.undp.org/en/composite/HDI), sub-Saharan Africa has the lowest HDI of 0.537. Hence, to improve these statistics sub-African countries tends to place more emphasis on the social and economic sustainability criteria, unlike other countries focusing on environmental sustainability criterion. Given these limitations in these existing green rating tools, existing knowledge, and practice which include in summary – (1) the neglect of the economic and social sustainability criteria (by existing rating tools) in the sustainability assessment of building projects (Illankoon et al., 2017; Olawumi et al., 2018); (2) the unsuitability of these existing rating tools to the climate and geographically characteristics of the sub-Saharan region of Africa (Nguyen & Altan, 2011); (3) the higher priority given to some of the environmental criteria of these existing green rating tools (Alyami & Rezgui, 2012; Xiaoping et al., 2009); and (4) the lack of a wholly-developed technological tool to aid the sustainability assessment of green buildings (Du Plessis, 2007; Olawumi & Chan, 2020b), among others. A part of this study resulted in the development of the Building Sustainability Assessment Method (BSAM) scheme green rating system to suit the regional context of the countries within the subSaharan region of Africa (Olawumi & Chan, 2019c). Also, the BSAM scheme, unlike the other existing green rating tools, comprehensively included the three pillars of sustainable development (Olawumi & Chan, 2019c). 14 More so, as regards the development of a technological tool to aid the sustainability assessment of green buildings, Deakin and Reid (2018) argued the need for future internet-based (cloud) infrastructures to handle the data management, and sustainability ranking and assessment of green buildings as well as smart cities. Also, Stratigea et al. (2015) highlighted the roles and potential of enabling digitization tools and technologies to gather and process information necessary to aid the capacity decision-makers in the built environment and solve related urban sustainability issues. Meanwhile, Campbell (2016) argued the need for decisionmakers and relevant stakeholders to consider a holistic consideration of the sustainability – economic, environmental, and social sustainability – in advocating and promoting green building and cities. Therefore, with the development of the BSAM scheme green rating system in this study and to bridge the above gaps in extant literature and practice; the current study aims to develop a Cloud-based Sustainability Decision Support System (C-SDSS) platform to aid the assessment of the sustainability performance of green buildings and infrastructures as well as to promote the implementation of sustainability practices in building and real estate developments. The C-SDSS platform will be developed to facilitate the comparison of the sustainability performance of two or more buildings for sustainability decision making purposes. The C-SDSS platform was developed in this study using high-level programming languages (such as PHP, Jscript, etc.) as well as the use of relational databases such as MySQL. Also, the BSAM scheme will be integrated with the C-SDSS platform as its primary green building rating system. 1.3.6 Any necessity for the development of a Green-BIM Assessment Framework for Green Building Projects in Sub-Saharan Africa? In recent years, there has been an increase in the adoption and implementation of sustainable development and smart tools such as BIM (Olawumi et al., 2020; Olawumi & Chan, 2020a). These developments in the built environment – especially in buildings and cities – have led to a paradigm shift that has translated to practical strategies and concepts such as smart buildings and smart cities, green buildings, among others. (Lam & Yang, 2020; Olawumi & Chan, 2020c). The Green-BIM concept is also an emerging trend in the extant literature and involves the application of BIM tools to enhance sustainability adoption (Wu & Issa, 2014b) 15 Several verifiable benefits of the adoption of BIM have been identified in the literature (Abdirad, 2016; Antón & Díaz, 2014; Cemesova et al., 2015); as well as its capability to allow for the integration of several other processes and concepts such as sustainability considerations, knowledge management. For instance, Oti et al. (2016) and Tsai et al. (2014b) demonstrated the capabilities of BIM tools to assess sustainability parameters of building designs and models via the development of customized plugins. Yuan et al. (2019) and Olawumi and Chan (2018b) described BIM as an innovative system that allows the digitalization of building information throughout the building project lifecycle stages. Some buildings and cities have sprung up based solely on either the concepts of “eco,” which incorporates mostly sustainability measures or smart schemes (Cugurullo, 2017). However, there have been issues of unclear and chaotic standards/road maps by project teams attempting to implement and promote smart and sustainable practices (Lobos et al., 2019). Taylor Buck and While (2017) and Sala et al. (2015) further revealed the issues behind the discordant implementation of smart buildings and sustainability in the built environment stems from several factors. A limitation is the interoperability issues between design and energy simulation software programs (Adamus, 2013). Other potential barriers associated with the joint implementation of smart and eco-initiatives in construction projects were discussed in Olawumi et al. (2018). Conversely, there are practical benefits and positive effects of smart- and eco-initiatives, which include better occupants’ comfort and productivity, less carbon footprint, real-time environmental data monitoring, better design of buildings, among others (Ali & Al Nsairat, 2009; Lee et al., 2012a). A study by Jung and Lee (2015) shows a less than 10 percent application rate of the use of BIM for building sustainability analysis in Africa compared to about 40%, 54%, and 73% in Asia, Europe, and North America (see Figure 1.1). Another study by Olawumi et al. (2017) shows a low level of BIM adoption in Africa, which is consistent with the findings by Jung and Lee (2015). Findings in the extant literature show similar status for the adoption of sustainability practices in Africa compared to Europe and America (Olawumi & Chan, 2018a). Although steps have been taken by stakeholders in the sub-Saharan region to improve the adoption of BIM and sustainability – such as the development of the BSAM scheme (Olawumi & Chan, 16 2019c). However, the region is still faced with the challenge of digitalizing the sustainability assessment and other processes in the built environment (Olawumi et al., 2018; Saka & Chan, 2019). Figure 1.1: BIM usage for Building Sustainability Analysis (Jung & Lee, 2015) Olawumi and Chan (2019a) described Green-BIM as an innovative and emerging concept; while to (Wu & Issa, 2014b), it is perceived as a synergetic initiative which bridge and break the “aura of singularity” of the sole implementation of either smart initiative (which focus mostly on BIM usage) and eco-initiatives in the built environment. However, according to extant literature (see Antón & Díaz, 2014; Wu & Issa, 2014b), the lack of a clear strategic framework has hindered the adoption and implementation of green-BIM in the built environment, most notably in building projects. More so, per Olawumi and Chan (2020c), the development of a digital and automated system can drive the implementation of green-BIM, especially in the subSaharan region of Africa where its adoption is still quite low. Hence, the current study aims to propose and develop an integrated green-BIM assessment (GBA) framework to evaluate the sustainability performance of buildings and facilitate the implementation of smart sustainable practices in sub-Saharan Africa. Towards achieving the study’s aim, firstly, the key components that make up the proposed GBA tool will be highlighted. Also, the necessary documents required for the GBA tool’s components and at the different building development stages will be identified. More so, the proposed GBA tool will be validated to verify its suitability and applicability within the context of the sub-Saharan region. This study presents 17 one of the key stages of concerted research works to digitalize the sustainability assessment process in the sub-Saharan region of Africa. The research findings of the study are expected to have a broad range of applications and profound impacts on the knowledge and implementation of smart and sustainable practices in the built environment. The scope of the application of the proposed GBA framework is limited to the sub-Saharan region of Africa (see Figure 1.2) because one of its key components – the BSAM scheme – was designed specifically for the region as discussed in Chapters 7 and 8. More so, the subSaharan region based on the existing research studies (see Jung & Lee, 2015; Olawumi et al., 2017; Olawumi & Chan, 2017, 2018a) has been observed as one of the lowest adoption and implementation regions of smart- and eco-initiatives in the world. More so, the current research will discuss one of the smart tools- BIM. Although the other smart devices are being used in the construction industry, BIM is still the most widely employed smart tool (Jung & Lee, 2015; Olawumi et al., 2017; Wong & Zhou, 2015). 1.4 Research Aim and Objectives This study aims to develop a green-BIM assessment model and cloud-based sustainability decision support system for evaluating buildings' compliance to sustainability principles with a view to integrating smart sustainable practices in building construction and management, improving operational efficiency, and enhancing the overall implementation of sustainable development in the built environment. The scope of study mainly focuses on developing countries located in the sub-Saharan region of Africa – using Nigeria, the largest economy in the region as a case study – with practical applications to other regions. The following research objectives have been set out in fulfilling the study’s aim: 1. To identify and assess the inherent benefits, barriers, and critical success factors (CSFs) associated with integrating BIM and sustainability principles in building projects. 18 2. To establish the relative weightings of the key sustainability indicators, sustainability attributes, and sub-attributes for buildings. 3. To develop a sustainability evaluation index for buildings using the Generalized Choquet Fuzzy Integral method. 4. To develop a cloud-based sustainability decision support system (C-SDSS) for buildings. 5. To develop a conceptual Green-BIM assessment framework as a tool for the evaluation of sustainability performance of buildings. Figure 1.2: Map showing the sub-Saharan region of Africa 19 1.5 Research Approaches and Contribution to Knowledge This section outlines briefly describes the research approaches adopted towards achieving the aim and objectives of the study as well as the intended contributions to knowledge and practice. Detailed discussion and descriptions of the research techniques and the contributions of this study to knowledge and practice are expatiated in subsequent chapters of this thesis, as illustrated in section 1.6 and Figure 1.3. The overall research methodology can be classified into four phases (sections 1.5.1 – 1.5.4): 1.5.1 Literature review – extant literature, practice, and experts’ consultations The review of the literature is often the first stage in a research endeavor and very critical towards identifying the trends and practices in the research discipline as well as relevant gaps in the knowledge and practice of the subject matter. It also helps to establish a solid theoretical base for the study. The current study is an interdisciplinary research field – involving the concepts of BIM and sustainability. It was necessary to conduct an extensive review of the literature and practice involving either BIM or sustainability adoption and implementation in the built environment as well as its joint application. The review helped in highlighting the key benefits, barriers, and drivers of the implementation of BIM and sustainability practices in the construction industry (Objective #1). More so, since the study set out to develop a green building rating system suited to the regional context of sub-Saharan Africa. A review of existing rating systems, green building technical notes and guidelines was conducted to develop the BSAM scheme as well as establish its sustainability criteria (Objective #2). It was also useful in the comparative assessment of the BSAM scheme with other leading green rating systems such as LEED, BREEAM, BEAM Plus, etc. Moreover, the review of the various Multi-Criteria Decision Methods (MCDM) helped to decide to use the more superior weighting methodology – the Generalized Choquet Fuzzy Integral (GCFI) to develop the sustainability evaluation index of the BSAM scheme. The GCFI helped to identify the weight of the BSAM criteria – indicators, attributes, and sub-attributes – and the other factors used in the building sustainability assessment process in this study (Objective #3). Furthermore, a review 20 of the extant literature helped in the extraction of the six components aggregated to develop the GBA framework (Objective #5). Meanwhile, relevant informal discussions, brainstorming sessions (with research supervisors and academic colleagues), and expert consultations were held during the study to establish the aim and objectives, methodologies, assemble research data as well as validate the study’s findings and data. The literature review stage involves retrieving, synthesizing, analyzing, and classifying information from the relevant secondary data sources. 1.5.2 Primary data collection – Questionnaire surveys and Data analysis After the conclusion of the various aspects of the literature review phase, the second phase of the research approach, which involves the collection of relevant data via questionnaires surveys and expert consultation, was commenced. Several sets of questionnaire surveys were developed and can be categorized into (1) general surveys, (2) a GCFI-based expert surveys, and (3) validation surveys. For the general surveys, three sets of questionnaires – pilot, Delphi, and international expert surveys – were developed to achieve Objective #1. These sets of questionnaires helped to establish and highlights the key benefits, barriers, and drivers of implementing BIM and sustainability practices. The GCFI-based expert surveys consist of five sets of questionnaire forms that were distributed to experts in the subject matter towards identifying the BSAM scheme sustainability criteria (for new and existing buildings). These questionnaires also help in determining the degree of importance for each of its sustainability criteria; establishing the BSAM scheme certification grade system as well as the trapezoidal fuzzy numbers; and an appendix showing the structure of the BSAM scheme criteria – indicators, attributes, and sub-attributes. Based on the qualitative and quantitative data provided by the experts in the questionnaire surveys set, the building sustainability evaluation ratio (BSER) for new and existing buildings was obtained as well as the weighting of each criterion. It also helps to identify where and when to upgrade the overall BSER value of a project towards improving its sustainability performance. The data from the GCFI-based expert surveys were used to achieve Objectives #2 and #3 and as input towards achieving Objective #4. 21 The validation surveys were used to establish the applicability, suitability, credibility, etc. of the two models – BSAM scheme (Objective #3) and the GBA framework (Objective #5); and the C-SDSS platform developed in the study (Objective #4) within the built environment, most notably within the sub-Saharan region. 1.5.3 Case studies – Implementing the models and cloud-based platform As highlighted in section 1.5.2, two significant models were developed in the study which is: (1) the Building Sustainability Assessment Method (BSAM) scheme, which is a regional-based green building rating system suited to the context of sub-Saharan Africa (Objective #3). (2) Green-BIM Assessment (GBA) framework intends to serve as an automated and dynamic tool to facilitate the assessment of the sustainability performance of green buildings for comparison and benchmarking purposes (Objective #3. Also, the Cloud-based Sustainability Decision Support System (CSDSS) platform was developed (Objective #4). Apart from using validation surveys as illustrated in section 1.5.2, real-life case study building projects were used to validate the BSAM scheme green rating system and the C-SDSS platform. These involve collecting the BIM models as well as relevant physical data and sustainability data of the buildings. 1.5.4 Cloud-based system – Coding, database management, and web development A key deliverable of this study is the development of the Cloud-based Sustainability Decision Support System (C-SDSS) platform (Objective #4). The C-SDSS platform is designed to be a digitalized, automated green building sustainability assessment tool for green buildings projects. The key features and significance of the C-SDSS platforms are discussed in Chapter 9 of this thesis. Most of all, for its development and management, a number of high-level programming languages – mainly the PHP and Jscript – were deployed and used on Adobe Dreamweaver software. A relational database in the form of the robust MySQL databases was integrated to serve as the data storage and mining for the C-SDSS platform while the C-SDSS platform itself was hosted on a local cloud-based server. The primary function of the C-SDSS platform which is to provide an automated and dynamic system for decision-makers and assessors to evaluate the sustainability performance of green building projects. It also provides avenues for its users to 22 compare their building designs and projects for their sustainability potential or performance, respectively. The C-SDSS platform can also help pinpoint areas of the sustainability performance of a building that needs improvement. Also, the C-SDSS platform being an open-source project, it provides a cost-free solution to its users in the sustainability assessment of their building projects. Figure 1.3: Overall research approach 1.6 Structure of the Thesis This research study is structured into eleven chapters. Chapter 1 presents and discusses the background of the research, states the scope of the study, and the 23 research problems that give rise to each of the research objectives, states the research aim and objectives. It also briefly discusses the various research approaches adopted in this thesis. Chapter 2 presents a comprehensive review and a scientometric analysis of the concept of sustainability and sustainable development as it relates to the built environment. It also discusses the evolution of the research field from the definition of its concepts in the Brundtland Commission report to the recent development of models and sustainability criteria used in assessing green building projects. It also gives an extensive understanding of the salient research themes, trends and pattern of sustainability research worldwide. Chapter 3 presents the comprehensive reviews and bibliometric analysis of BIM literature towards investigating and understanding the pattern of BIM research. Network maps that display the visualization of the structure of BIM are also presented. It also provides relevant research areas that require considerations, and the discussion of selected research areas provide an extensive understanding of salient BIM fields. Since, the study reported in this thesis is a multi-disciplinary research work, Chapters 2 and 3 were designed to report mostly on sustainability and BIM respectively, although some sections in this chapters were designed to buttress on the linkage between these two salient concepts and how it applies to the construction industry and the built environment. Chapters 4, 5, and 6 identify and examines the benefits, impediments (barriers) to, and drivers of the implementation of BIM and sustainability practices in building projects as well as in the built environment, respectively. Two datasets were collected namely via Delphi surveys and international surveys. These three Chapters helped to fulfill Objective #1 of this research project. As shown in Figure 1.3, the key research outputs of objective #1 – which are the establishment of the key factors affecting BIM and sustainability practices implementation (Chapters 4 - 6) – formed and also helped in the development of some of the components of the conceptual Green-BIM assessment framework (Objective #5). Hence, Chapters 1 – 6 of this study helped in establishing the background of the research work and fulfilling Objective #1. 24 Chapter 7 develops a sustainability assessment method for building projects within the context of the sub-Saharan region of Africa – based on the findings and deliverables of the previous chapters. The developed Building Sustainability Assessment Method (BSAM) scheme and its weighted criteria will be validated using two real-life building case studies. The proposed BSAM scheme will be compared with six widely used green rating systems such as LEED, BREEAM, etc. in this chapter. It also provides an overview of the development of the BSAM scheme and its sustainability criteria. The findings of this chapter led to the fulfilment of Objective #2. As shown in Figure 1.3, the key research outputs of Objective #2 presented in Chapter 7 of this thesis is the development of the relative weightings and structure for the BSAM scheme sustainability criteria which was used in Objective #3 to develop the sustainability evaluation index (using the GCFI method) for the BSAM scheme as reported in Chapter 8 (Objective #3) of this thesis. Chapter 8 employs the MCDM technique – the generalized Choquet fuzzy integral (GCFI) method – to determine the weights of the sustainability criteria and develop the sustainability evaluation index of the BSAM scheme, while solidifying the development of the BSAM scheme. The developed sustainability rating model (BSAM scheme) will then be validated in four real-world building case studies to demonstrate its usefulness and robustness in practice. More so, the significance of the GCFI technique over other weighting methodology and MCDM tools will be discussed. The findings of this chapter led to the fulfilment of Objective #3. The key research outputs of Objective #3 as shown in Figure 1.3 is the establishment of the sustainability evaluation index for the BSAM scheme which along with the key deliverables of Objective #2 formed the basis of the assessment of the sustainability performance of building projects on the developed Cloud-based Sustainability Decision Support System (C-SDSS) as presented in Chapter 9 (Objective #4) of this thesis. The BSAM scheme was the primary green building rating system (GBRS) on the CDSS platform. Chapter 9 develops a C-SDSS to facilitate the assessment of the sustainability performance of green buildings. The C-SDSS platform will be developed using various high-level programming languages such as PHP, Jscript, etc. and relational 25 databases. The primary green building rating system to be used on the C-SDSS platform is the BSAM scheme – which was developed in chapters 7 and 8. It was purposely explicitly designed for the sub-Saharan region of Africa and which holistically considered the social, economic, and environmental sustainability criteria. Also, the proposed C-SDSS platform will permit the comparison of green building projects’ sustainability credentials on the cloud-based system. The findings of this chapter led to the fulfilment of Objective #4. The key research output of Objective #4 is the development of the CSDSS platform which incorporates the BSAM scheme as its primary GBRS. The BSAM scheme incorporated within the C-SDSS platform utilizes the sustainability evaluation index earlier developed in Chapter 8 (Objective #3). The C-SDSS platform and relational database (Objective #4), and BSAM scheme (Objectives #2 & #3) formed parts of the components of the Green-BIM Assessment Framework developed as presented in Chapter 10 (Objective #5) of this thesis. Chapter 10 combines the various deliverables and findings from the previous chapters to develop a holistic Green-BIM Assessment (GBA) Framework for green building projects in Sub-Saharan Africa. A conceptual research framework approach based on a consolidated desktop literature review forms part of the basis of the GBA framework development. The GBA framework is expected to serve as an automated and dynamic digital tool for the evaluation of the sustainability performance of green building projects for comparison and benchmarking purposes. Three of the six components of the GBA framework are based on the deliverables of chapters 7, 8, and 9, hence developed beyond the ‘conceptual’ level. The findings of this chapter led to the fulfilment of Objective #5. Chapter 11 concludes this thesis by providing a review of the research objectives and highlighting the significant contributions of the study, as well as recommendations for future research, are also discussed. 1.7 Chapter Summary This chapter has presented and discussed the background of the research, scope of the study, and the research problems, and stated the research aim and objectives. It illustrated the various research approaches adopted and the contribution of the study. This chapter also presented the structure of the thesis. The next two chapters 26 (Chapters 2 and 3) will introduce via a review of the extant literature the concept of sustainability and BIM respectively. The following chapter presents a comprehensive review and a scientometric analysis of the concept of sustainability and sustainable development as it relates to the built environment. 27 CHAPTER 2: A SCIENTOMETRIC REVIEW OF GLOBAL RESEARCH ON SUSTAINABILITY AND SUSTAINABLE DEVELOPMENT2 2.1 Chapter Overview The previous chapter introduced the research and presented the aim and objectives of the study alongside the relevant gaps in knowledge and practice to be bridged in this research. It also outlined the scope of the study. The current chapter presents a comprehensive review and scientometric analysis of the concept of sustainability and sustainable development as it relates to the built environment based on 2094 corpus data. More so, this chapter discusses the evolution of the research field from the definition of its concepts in the Brundtland Commission report to the recent development of models and sustainability criteria used in assessing green building projects. It also gives an extensive understanding of the salient research themes, trends and pattern of sustainability research worldwide. 2.2 Introduction The fulcrum for the worldwide attention being paid to the concept of sustainable development (SD) was the Brundtland Commission report of 1987 which help defined SD as seeking “to meet the needs and aspirations of the present without compromising the ability to meet those of the future” (WCED, 1987). However, there have been challenges in meeting some of the thresholds of SD due to the limitation imposed by the social issues, technological advancement and the ability of the ecosystem to accommodate human carbon footprints. Therefore, it is unrealistic to have a single SD blueprint for every country or region. Hence, each country would need to develop its SD policies and standards but with a global objective in mind. As noted by Axelsson et al. (2011), sustainability and SD are two concepts that have gained reception at national and global levels due to challenges and risks faced in areas such as rural development, environmental conservation, energy, climate change, human wellbeing etc. Hence, in recent years there have been a shift in focus and action plans to address these problems. SD is currently adopted as a This chapter is fully published in: Olawumi, T.O., & Chan, D.W.M. (2018a). A Scientometric Review of Global Research on Sustainability and Sustainable Development. Journal of Cleaner Production, 183, 231–250. https://doi.org/10.1016/j.jclepro.2018.02.162 2 28 growth strategy in the built environment. According to Sartori et al. (2014), sustainability is described as a process and mechanism to achieve the intended sustainable development; while according to Dovers and Handmer (1992), it is a process of “intentional change and improvement”. As noted by Norton (2005), the two terms of sustainability and SD are often used interchangeably, however, Axelsson et al. (2011) argued that the two concepts are quite different. Axelsson et al. (2011) described sustainability as a policy vision of the society with primary purpose of preventing the depletion of natural resources. Clark (2002) however, observed that the issue of what sustainability means is more complex and per Parrotta et al. (2006) and Ramakrishnan (2001), it currently involves issues such as biodiversity conservation, ecological integrity etc. In contrast, as stated by Axelsson et al. (2011), SD is more of a collective societal process that involves multiple stakeholders with differing salience level and powers. Nevertheless, Lee (1993) described both concepts as a “social learning and steering process” which involved both management and governance mechanism. The concept of sustainability is conceptual (Ekins et al., 2003) and hence easily misunderstood, although still hugely popular (Slimane, 2012). SD is however multidimensional in scope (Slimane, 2012), an integrated concept (Sartori et al., 2014) and based on the principles of sustainability (Dovers & Handmer, 1992). SD also helps to find a balance between preserving the ecosystem and meeting human needs. The three pillars of SD are environmental, social and economic sustainability; and these constructs must be harmonized to achieve a holistic SD. Environmental sustainability is concerned with confining human activity within the carrying capacity of the ecosystem (such as materials, energy, land, and water, etc.) prevailing in the locality and places emphasis on the quality of human life (air quality, human health). Moreover, the economic sustainability considers the efficient use of resources to enhance operational profit and maximize market value. It also deals with substituting natural for manmade resources, reuse, and recycling. However, the social sustainability focuses on the social well-being of the populace, balancing the need of an individual with the need for the group (equity), public awareness and cohesion, and participation and utilization of local labors and firms. Sartori et al. (2014) acknowledged that the approach to sustainability defers based on the field of application, such as engineering, management, ecology, etc. Sala et al. (2015) 29 considered sustainability assessment as an appraisal method to evaluate the level of the implementation of these sustainability measures. The sustainability assessment results will be used for decision-making and policy formulation for real-world SD applications (Hacking & Guthrie, 2008). Several studies have been published to addressed salient challenges facing sustainability in the built environment. Ahmad and Thaheem (2017) developed a social sustainability assessment framework for residential buildings using a weighted aggregation approach to improve its performance value. Also, Ahmadian et al. (2017) and Akanmu et al. (2015) utilized a Building Information Modelling (BIM)based approach to address sustainability issues regarding material selection and supply decisions. Moreover, Damtoft et al. (2008) discussed issues relating to climate change initiatives and SD. Meanwhile, studies (see Akinade et al., 2015; Althobaiti, 2009; Forsberg & von Malmborg, 2004; Gao et al., 2015; Huang et al., 2010b; Wang et al., 2015a); attempted to integrate technological and innovative tools to advance the concept of sustainability and SD. 2.2.1 Knowledge gap, research objectives, and value Sustainability is a wide and complex research field which several applications in different disciplines and industries. However, previous review papers on sustainability in the built environment have focused mainly on environmental sustainability, a gap which the current study tends to bridge. For instance, Wong and Zhou (2015) examined the concept of green BIM and sustainability across the various stages of building development. The authors examined the research frontiers of green BIM and proposed a ‘one-stop-shop’ BIM for environmental sustainability. Also, Darko et al. (2017) classified the drivers of green building and categorize them into five (5) sub-levels such as external drivers, property-level drivers, corporatelevel drivers, project-level drivers, and individual-level drivers. Both Wong and Zhou (2015) and Darko et al. (2017) used the Scopus database. Similarly, Falkenbach et al. (2010) reviewed the drivers for sustainable building by examining the perspective of various stakeholders in the real estate market. Aarseth et al. (2016) carried out a systematic literature review (SLR) and highlighted several project sustainability strategies that could be employed in project organizations to enhance project performance. Lele (1991) carried out a critical review of the concept 30 of SD and discusses the idea in relation to issues such as economic growth, environmental degradation, community participation, and international grade. However, the review didn’t include discussions of extant literature as sustainability was still a relatively new concept as of the time. Also, the previous studies such as Wong and Zhou (2015), Aarseth et al. (2016) and Darko et al. (2017) analyzed 84, 68 and 42 journal papers respectively as compared to a relatively higher corpus of papers in this study (2094 articles). Moreover, no previous review of the sustainability research corpus mapped out the linkage or working relationships among the clusters of sustainability researchers and their institutions. Also, no previous studies have analyzed its research corpus to such depth to include aspects such as co-citation clusters, keywords, or research clusters. Given the above, this study aims to bridge these gaps in extant literature by undertaking an in-depth scientometric review of the global on the sustainability and SD; with a view to providing researchers and practitioners with a comprehensive understanding of the status quo and research trend in its research, with a focus on the three pillars of sustainable development. Therefore, to achieve the study aim, five scientometric techniques will be employed as discussed under section 9.2.3 which will be used to (i) track the evolution of the sustainability research field, (ii) identify the key researchers and institutions. Also, part of the objectives of this study is to (iii) identify the key subject categories, (iv) research keywords and co-citation clusters as well as (v) deduce the salient and emerging research themes. Meanwhile, a large corpus of journal articles (2094 bibliographic records) would be analyzed, which is a significantly high volume of articles than previous reviews on sustainability or elsewhere. The findings of the study are expected to contribute to the existing body of knowledge by highlighting the trend and pattern of sustainability research field, establishing its research themes and clusters, mapping the network of key sustainability researchers and institutions and recommending areas for future studies. It will also serve as a consultation toolkit for policy making for government agencies. 2.3 Research Methodology The study carried out a scientometric review, analyses, and visualization to achieve the predefined research objectives of providing the academics and industry 31 practitioners an in-depth understanding of the structure (clusters), research areas and trending topics in sustainability’s studies in the built environment aided with illustrative diagrams and maps. The scientometric analysis is described as one of the most used methods to evaluate and examine the research development and performance of academics, faculties, colleges, countries and even journals in an identified research field (Konur, 2012). The scientometric analysis is a technique that allows for a broader yet concise capturing and mapping of a scientific knowledge area by identifying structural patterns and tracing salient research frontiers using mathematical formulae and visualization. Moreover, other scientific methods such as bibliometric technique (Albort-Morant et al., 2017; Olawumi et al., 2017; Santos et al., 2017); content analysis (Park & Cai, 2017); literature reviews (Wong & Zhou, 2015); latent semantic analysis (LSA) (Yalcinkaya & Singh, 2015); and scientometric analysis (Montoya et al., 2014; Zhao, 2017); have been used by several authors across research areas such as green building and innovation, building information modelling (BIM), publicprivate partnerships (PPPs), energy, and sustainability. Five scientometric techniques would be adopted in this study. (1) Co-Author analysis: This includes co-occurrences of authors, countries/regions and faculties/institutions in the indexed corpus of journal articles. (2) Co-Word analysis: This identifies co-occurring keywords or terms and co-occurring Web of Science (WoS) subject. (3) Co-Citation analysis: This analysis includes co-cited authors, cocited articles/documents, and co-cited journals. (4) Clusters analysis: This includes burst detection analysis and silhouette metric analysis. (5) Geospatial analysis: Geospatial network visualization (animated maps) of journal articles and authors’ origin and generation of Keyhole Markup Language (KML) files for use in using Google Earths. The above five (5) scientometric analysis and its visualization could be performed using a software package “CiteSpace” developed by Chaomei Chen. CiteSpace version 5.0.R7 (32bit) was used to analyze the indexed corpus articles because per Chen (2016), CiteSpace is very useful in mapping knowledge domains and aiding its illustration with graphical maps. More information on how to utilize the software “CiteSpace” for scientometric reviews of a research field are available in the 32 literature (see Chen, 2016, 2014, 2005a; Chen and Morris, 2003). Figure 2.1 depicts the study’s research design. Figure 2.1: Outline of Research Design 33 2.3.1 Literature search strategy and Research data One of the decisions to make in undertaking an analysis of a knowledge domain such as in this study is for the researcher(s) to identify scientific databases to use. The three primary scientific databases are Scopus, ISI Web of Science and Google Scholar, Olawumi et al. (2017) provided a comparative assessment of strength and weakness of these three databases. Similarly, several core journals publishing houses have their databases such as those of Elsevier- Science Direct, ASCE Library, Emerald, Wiley Online Library, ProQuest, EBSCO, Taylor & Francis, Springer Link, IEEE Explore among several others available for journal search and retrieval (JSR). Nevertheless, based on the submission of previous authors (Marsilio et al., 2011; Neto et al., 2016; Olawumi et al., 2017; Zhao, 2017); Web of Science core collection database was adopted for this study’s JSR. It is because WoS is regarded as the most comprehensive and it also contains the most relevant and influential journals in its record combined with WoS scientific robustness. A comprehensive literature search, retrieval, and indexing were carried out on WoS core collection using the search string- “sustainability* and sustainable development*” as seen in Figure 2.2. A fuzzy search is denoted with a “*” and the selected time-span ranges from 1991 – 2016 (26 years).The search results were refined to include only journal articles and articles written in the English language because published journal articles would have undergone a thorough peer review process and most authors do republish their conference papers and thesis in scholarly journals afterward (Olawumi et al., 2017). Journal articles are regarded as more reputable sources and also classified as “certified knowledge” (RamosRodríguez & Ruíz-Navarro, 2004) and are more comprehensive than other sources (Ke et al., 2009; Yi & Chan, 2013; Zheng et al., 2016). CiteSpace meanwhile uses several databases as its source of data such as WoS, Scopus, PubMed among others but do convert such data from other sources to WoS format before processing the data. Hence, Chen (2016) advise the use of WoS database for use in JSR to prevent loss of data during the conversion process and reduce the processing time. 34 Figure 2.2: Literature search and indexing strategy Moreover, sustainability research areas which are not relevant to the built environment were excluded from the search results. Mainly research areas such as “Environmental Science Ecology” “Engineering,” and “Construction Building Technology” were retained. A total of 2094 bibliographic records were collected in September 2017, and the articles were then downloaded and indexed into Mendeley reference manager. Also, the CiteSpace software was installed, and the WoS records captured, saved in WoS “Marked List” and downloaded and inputted as research data for use as explained in the CiteSpace manual (Chen, 2014). The first paper on sustainability was in 1991 which focused on developing legislation and standards to control wood processing in Australia (Gifford & McFarlane, 1991) which has two citations so far and focused on the environmental aspect of sustainability. Figure 2.3 shows the distribution of the 2094 bibliographic records from the year 1991 to 2016. 35 400 355 369 Number of Articles 350 300 222 230 250 200 150 109 100 50 66 3 2 2 2 8 124 76 74 68 80 77 31 36 29 20 12 18 27 23 27 0 Year Figure 2.3: Distribution of the indexed research corpus from 1991 – 2016 The number of articles on sustainability increased significantly between 2011- 2016 and it crossed the 100 articles per year threshold in the year 2011 and subsequently crossed the 200 articles and 300 articles per year thresholds in 2013 and 2015 (2year intervals). 2.4 Scientometric Analysis, Results and Discussion This section discusses the facets and results of this study’s scientometric analysis as described in the research design (Figure 2.1). The following sections entail the coauthor analysis, co-word analysis, co-citation analysis, clusters analysis and geospatial analysis. Since the study is examining a lengthy period of research (1991 – 2016), time slicing was employed. According to Chen (2005b), time slicing is a “divide-and-conquer strategy that divides a period into a series of smaller windows.” A 2-year per slice was used for co-author analysis, co-word analysis, co-citation analysis and clusters analysis while a 1-year per slice was used for the geospatial analysis. The Pathfinder utility in CiteSpace was used to prune the network to remove redundant links through the process otherwise known as ‘network pruning.' Moreover, among the pruning utilities in CiteSpace, Pathfinder is regarded as the 36 better choice (Chen, 2014) and details of its pros and cons are explained in Chen and Morris (2003). 2.4.1 Co-author analysis Information available from the WoS records in this research field contains relevant details about the authors which are useful in establishing prolific authors, institutions or faculties and countries. Hence, such data can be extended to evaluate networks of co-authors, a network of countries or regions and those of institutions. 2.4.1.1 Co-Authorship Network An analysis of the most productive authors (see Table 2.1) reveals Donald Huisingh (University of Tennessee, Knoxville), Rodrigo Lozano (University of Gävle) and Yong Geng (Shanghai Jiao Tong University) as the three researchers with most publications in the field. Table 2.1: Top 13 most productive authors with their h-index Authors Institution Country Donald Huisingh Rodrigo Lozano Yong Geng Per Angelstam Roland Scholz Adisa Azapagic James Mihelcic Sekar Vinodh Xiaoling Zhang Tomas Ramos Marine Elbakidze Robert Axelsson Rebeka Lukman University of Tennessee, Knoxville University of Gävle Shanghai Jiao Tong University Swedish University of Agricultural Sciences Swiss Federal Institute of Technology, Zurich University of Manchester University of South Florida National Institute Technology Tiruchirappalli City University of Hong Kong Counts h-index USA Sweden China Sweden 25 16 10 7 29* 29 49 54 Switzerland 7 53 UK USA India 7 7 7 42 37 26 7 22 7 21 7 20 7 19* 7 10* Hong Kong SAR Universidade Nova de Lisboa, Portugal Lisbon Swedish University of Agricultural Sweden Sciences Swedish University of Agricultural Sweden Sciences University of Maribor Slovenia Note: * - the h-index of the authors are based on ResearchGate.net calculation while the other authors h-index are based on Google Scholar. 37 A co-authorship network was generated as shown in Figure 2.4 identify the network of authors represented by nodes and links. Each representative node represents each author while each link represents the pattern of collaboration established in the publications (Zhao, 2017). The network was pruned as before described resulting in 144 nodes and 99 links in the co-authorship network. The node size corresponds to the number of publications by each author while the thickness of the links represents the strengths of ‘cooperative relationships’ among the author. The co-authorship network has a Modularity, Q= 0.942 and a mean Silhouette, S= 0.470. The modularity (Q) and the mean silhouette values (S) reveals the “overall structural properties” of the network, that is a very high Q value (say Q>0.70) denotes loosely assembled clusters while the S-metric measures the homogeneity of the clusters (Chen, 2014). Hence, the dispersed nature of the clusters of authors within the network as seen in Figure 2.4. Meanwhile, the color of the links (e.g., blue, green, yellow and red) corresponds to the color encoding of the different time span in a 2-year slice as seen above the coauthorship networks. Moreover, regarding collaborative relationships and workings in the field, the network established several research communities constituted by central authors of the research community and other authors in the community. Three main research communities with robust collaboration among the authors include the highly productive research circuit of Donald Huisingh and Rodrigo Lozano as the central authors and other researchers such as Maik Adomssent, Liyin Shen, Jana Dlouha, Gyula Zilahy, and Kunhui Ye. Another research community with Yong Geng and Tsuyoshi Fujita as the central authors of the circuit including Huijuan Dong, Zhe Liu, Jingzheng Ren, and Liang Dong. Lastly, Robert Axelsson and Per Angelstam as the central authors of a research community which includes Kjell Andersson and Marine Elbakidze as authors within the circuit. 2.4.1.1.1 Citation bursts and centrality scores The impact of the authors and collaboration was analyzed using the citation burst and betweenness centrality. The citation burst is based on Kleinberg’s algorithm (Kleinberg, 2002) and it measures the increase in citations within a short time span. Two authors have citation bursts, which are Donald Huisingh (burst strength= 3.43, 2013–2016) and John Cairns (burst strength= 3.42, 1991–2000). Also, the 38 betweenness centrality which is based on Freeman’s work (Freeman, 1977) is defined as the degree to which “a point [or node] falls on the shortest path between others and therefore has a potential for control of communication.” Centrality scores in CiteSpace are normalized in the unit interval between 0 and 1 (Chen, 2014), and a node of high centrality score is one that connects two or more large groups of nodes in the network with the node itself in-between and it is denoted by purple trims in the network. Such nodes with high betweenness centrality form the basis of separating clusters (Girvan & Newman, 2002) and helps to identify pivotal and salient scientific publications over time. In Figure 2.4, Donald Huisingh (centrality = 0.02), Rodrigo Lozano (centrality = 0.01), Xiaoling Zhang (centrality = 0.01), Kim Ceulemans (centrality = 0.01), Andrew Barton (centrality = 0.01) and Heloise Buckland (centrality = 0.01) are the nodes with purple trims and they serve as links between different authors and research communities. It is noteworthy that Donald Huisingh is also the most productive author in the field, with the strongest citation burst and more connections and collaborative relationships with several researchers in the field. Figure 2.4: Co-authorship network 39 2.4.1.2 Network of institutions/faculties and countries/regions This section explores the contribution of institutions and countries to the body of knowledge in the field. The network generated 49 nodes and 99 links with modularity, Q=0.466 and a mean Silhouette, S= 0.589. Since the Q-value of the network is below average, the nodes within the network are densely packed (see Figure 2.5). Eight (8) countries were identified in the network (Figure 2.5) with a greater contribution (more than 100 articles) to the research area of sustainability and SD. These include the USA (428 articles, 20.44%); China (275 articles, 13.13%); United Kingdom (258 articles, 12.32%); Canada (157 articles, 7.50%); Germany (132 articles, 6.30%); Netherlands (131 articles, 6.26%); Australia (128 articles, 6.11%), and Sweden (124 articles, 5.92%). These results revealed the advanced level of research and development in sustainability studies in these countries and with most of the counties being European countries. It is noteworthy that countries such as the USA, the origin of the world-renowned building rating system (LEED- Leadership in Energy and Environmental Design) have the most articles on sustainability field and several building energy simulation software and devices originated from the US. In the United Kingdom, we have another building rating system (BREEAM-Building Research Establishment Environmental Assessment Method) while in Australia, we have the Green STAR building rating system. In respect to collaborative research, authors from countries such as the USA, China, the UK, Canada, Sweden, South Korea, Netherlands, Australia, Switzerland have strong international collaborations. Furthermore, in terms of institutions and faculties research outputs. The research on sustainability has progressed significantly in several universities among which are Chinese Academy of Sciences, China PR (67 articles), Delft University of Technology, Netherlands (37 articles), University of British Columbia, Canada (30 articles) Wageningen University Research, Netherlands (28 articles). The University of Tennessee Knoxville and the University of Tennessee System, both in the USA (25 articles each); ETH Zurich, Switzerland and Lund University, Sweden (24 articles); the United States Environmental Protection Agency, USA and the University of Leeds, United Kingdom (23 articles). Also, we have the Hong Kong Polytechnic University, Hong Kong SAR and the State University System of Florida, USA (22 articles) and the University of California, USA (20 articles). These institutions are unique in their outputs of research in the field of sustainability. 40 2.4.1.2.1 Citation bursts and centrality scores Moreover, significant citation bursts were identified in some countries as shown in Table 2.2. While for institutions, we have Chinese Academy of Science (burst strength= 5.40, 2009–2010), University of British Columbia (burst strength= 4.66, 1999–2006) and Lund University (burst strength= 4.37, 2005–2006). It is evidently clear from the citation burst analysis that there was no citation burst between 2015– 2016 for both countries and institutions; which is consistent to the fact that sustainability studies have garnered worldwide attention and consideration in recent years; one of which culminated in the signing of the Paris climate change which was signed by 166 countries. Hence, it would be difficult for a country or institutions to receive high citations in that period. Table 2.2: Countries' citation burst Countries Burst strength Span Japan 9.66 2011 – 2014 Brazil 8.16 2009 – 2014 Switzerland 5.71 1999 – 2012 Greece 5.15 2003 – 2008 United Kingdom 5.03 1991 – 2002 South Africa 4.66 1999 – 2006 Spain 4.30 2009 – 2010 Malaysia 4.13 2011 – 2012 Sweden 4.03 2005 – 2006 Denmark 3.44 1997 – 2008 More so, in terms of high between centrality as identified by purple trims in the network (Figure 2.5). The network revealed countries such as United Kingdom (centrality = 0.54), Sweden (0.49), the USA (0.47), Netherlands (0.40), Canada (0.18), China (0.12), Germany (0.12) and France (0.10). For institutions, we have the Imperial College London (centrality = 0.08), University of Oxford (0.03), University of Salford (0.02) and Lund University (0.01) with strong connections and acting as key exchange platforms between the countries and institutions. 41 Figure 2.5: Network of Countries and Institutions 2.4.2 Co-word analysis Several research topics and themes have merged and evolved in sustainability research over the decades which represents the trends and frontiers in the field. Data from the WoS bibliographic records are evaluated to develop the network of cooccurring keywords and subject categories in the sustainability field. 2.4.2.1 Network of co-occurring keywords Keywords are descriptive and significant words and serve as a reference point in finding and understanding the concepts and contents of research articles. It also reveals the development of the research field over time (Zhao, 2017). Two kinds of keywords are obtainable from the WoS bibliographic records which are the (i) author keywords and the (ii) keywords plus. The former is provided by the authors in their articles while the other is based on the journal’s classification of the research output. The two kinds are utilized in developing the network of co-occurring keywords in CiteSpace, and the software has a utility to merge similar keywords. A research network of co-occurring keywords as shown in Figure 2.6 with 71 nodes and 136 42 links. Also, the network has a modularity (Q = 0.523) and mean silhouette, S= 0.769. The node size for each keyword is a representative of the frequency of the keyword in the record. Meanwhile, the co-word analysis reveals high-frequency keywords (Figure 2.6) in the dataset which are “sustainability” (frequency = 778), “sustainable development” (frequency = 472), “management” (frequency = 212), “system” (frequency = 193), “indicator” (frequency = 141), “framework” (frequency = 112). Other high-frequency keywords include “China” (frequency = 89), “model” (frequency = 89), “energy” (frequency = 88), “performance” (frequency = 84), “impact” (frequency = 82), “climate change” (frequency = 53), “environment” (frequency = 44) and “design” (frequency = 43). 3.2.1.1 Citation bursts and centrality scores Fourteen (14) keywords were identified from the network with citation bursts as shown in Table 2.3. Table 2.3: Keywords' citation bursts Keywords Burst strength Span Environment 14.15 2004 – 2012 Climate change 13.82 2009 – 2014 Design 13.01 2013 – 2014 City 11.82 2013 – 2014 Policy 10.34 2013 – 2014 Sustainable development 8.88 1999 – 2006 Impact 7.40 2013 – 2016 Construction 6.95 2005 – 2008 Sustainability indicator 5.40 2003 – 2006 Industrial ecology 5.34 1998 – 2008 Innovation 5.10 2007 – 2008 Energy 4.42 2009 – 2012 LCA 4.26 2003 – 2010 Sustainable building 3.81 2005 – 2006 All these keywords with citation bursts represent the salient topics and themes in sustainability studies and research. It is noteworthy that keywords such as “climate change,” “design,” “energy,” “sustainable development,” “sustainability indicator,” 43 “environment” and “policy/framework” have both high frequencies and citation bursts. It is consistent with the fact that more efforts are devoted to these critical research themes which are pivotal in achieving a sustainable urban development. Several keywords also have high betweenness centrality scores and these include: “sustainability” (centrality = 0.80), “sustainable development” (0.64), “indicator” (0.25), “system” (0.21), “China” (0.20), “management” (0.19), and “environment” (0.17). Other keyword with high betweenness centrality are “public policy” (0.16), “framework” (0.12), “research policy” (0.11), “natural capital” (0.08), “decision making” (0.08), “energy” (0.07), “city” (0.06) and “ecological footprint” (0.06). These keywords and themes have greatly influenced the development of the sustainability research field and help connect several research topics. Figure 2.6: Network of co-occurring keywords 2.4.2.2 Network of co-occurring subject categories The bibliographic records in WoS database are classified into subject categories depending on the scope of the corresponding journal, and an article could be assigned one or more subject categories. A network of co-occurring subject categories was developed as shown in Figure 2.7 with 22 nodes and 61 links. The 44 modularity, Q = 0.467 and with a mean silhouette value, S = 0.534. The node size for each subject category is a representative of the number of articles classified within each category in the dataset. Eight (8) subject categories with 100 articles or more were identified: Environmental sciences (1327 articles); green & sustainable science technology (1294 articles), environmental engineering (925 articles); civil engineering (410 articles), environmental studies (376 articles); construction & building technology (254 articles), ecology (203 articles), and water resources (161 articles). A significant sustainability research articles have been published under these subject categories. Meanwhile, a look at the generated network and the color of the links reveals increasing publications in the area such as urban studies, computer science and interdisciplinary applications, architecture, ergonomics, and transportation. A study by Kerebih and Keshari (2017) which employed GIS to develop a numerical model for groundwater flow is a good example of the application of computer-based technology in technology research. Other studies (Khan et al., 2017; Stuermer et al., 2017; Wang et al., 2017; Xia et al., 2017) integrated technology-based application for sustainability research. For urban studies, Kamal and Proma (2017) modeled a quantitative ranking system for sub-urban Texas using GIS while Boren et al. (2017) proposed a sustainable transport system and roadmap for southeast Sweden. Meanwhile, Zamani et al. (2012) advocated for green architecture to reduce environmental pollutions and Ruiz-Larrea et al. (2008) recommended that sustainable concepts (e.g., energy efficiency) be integrated into the design of structures as it would key to sustainable industrialization. 3.2.2.1 Citation bursts and centrality scores Moreover, some subject categories received citation bursts: environmental studies (burst strength= 23.98, 2014–2016), water resources (burst strength= 20.80, 1993– 2009), construction & building technology (burst strength= 11.93, 1998–2002), chemical engineering (burst strength= 9.61, 2000–2007) and civil engineering (burst strength= 5.16, 2000–2002). Other subject categories with citation bursts are transportation (burst strength= 4.80, 2001–2010), ecology (burst strength= 4.44, 2009–2010) and industrial engineering (burst strength= 3.41, 2005–2010). These categories represent the most active areas in the evolution of sustainability research. 45 Areas such environmental sustainability have received significant citations in recent years (2014-2016), and this aligns with the findings of Olawumi et al. (2017). Also, some subject categories nodes received high betweenness centrality score as indicated by purple trims in the network (Figure 2.7) and these include engineering (centrality = 0.77), civil engineering (0.63), environmental science & ecology (0.46), environmental sciences (0.26), environmental engineering (0.23), computer science (0.22), and construction & building technology (0.18). They connect the distinct aspects and concepts in the research field and are pivotal in the development of the field. Figure 2.7: Network of co-occurring subject categories 2.4.3 Co-citation analysis Co-citation is the number of instances in which two items, say in this case, authors, documents, or journals are cited by a journal article (Chen, 2005a; Small, 1973) and described by Zhao (2017) as a “proximity measure” for the items. Indexed bibliographic records from WoS database are analyzed to produce the journal cocitation network, author co-citation network, and the document co-citation network. 46 2.4.3.1 Journal co-citation network The 2094 WoS bibliographic records used for this study are sourced from a hundred and thirty-eight (138) journals; with thirty-seven (37) journals having at least ten (10) records in the research corpus. The structure of the published research corpus on sustainability studies is consistent with the Pareto principle in that 1764 articles (84 percent) are published in 28 journals (20 percent) which relate to an 84/20 rule for this study’s research corpus. Table 2.4 shows the top 20 source journals for sustainability research along with their impact factors (IF). Meanwhile, the publishers in the USA and Netherlands account for six (6) and (5) journals of the top 20 source journals. The references cited by each of the 2094 indexed research corpora were analyzed and was used to generate a network of co-cited journals with 69 nodes and 133 links to identify the most significant cited journal as shown in Figure 2.8. The network has a modularity (Q = 0.53) and mean silhouette, S= 0.80. The node size is a representative of the co-citation frequency of each journal within the dataset. Moreover, the co-citation frequency of the top five most co-cited journals as revealed within the network are Journal of Cleaner Production (frequency = 722); Ecological Economics (frequency = 482), Journal of Environmental Management (frequency = 312); Science (frequency = 300), and Energy Policy (frequency = 278). These journals have made significant contributions to sustainability studies, and hence they are more cited by researchers in the field. 47 Table 2.4: Top 20 source journals in the research corpus Source Journal Host Country Journal of Cleaner Production USA Sustainability Switzerland International Journal of Sustainable USA Development and World Ecology Sustainability Science Japan Impact Factor (IF) 5.715 1.789 1.864 Publisher Count Percentage Elsevier Sci Ltd MDPI AG Taylor & Francis Inc 496 371 176 23.69 17.72 8.40 3.429 Springer Japan KK 56 2.67 Ambio Water Science and Technology International Journal of Life Cycle Assessment Sweden United Kingdom Germany 3.687 1.197 3.173 Springer IWA Publishing Springer Heidelberg 52 46 46 2.48 2.20 2.20 Resources Conservation and Recycling Proceedings of The Institution of Civil Engineers Engineering Sustainability Clean Technologies and Environmental Policy Building and Environment Netherlands United Kingdom 3.313 0.341 Elsevier Science BV ICE Publishing 41 40 1.96 1.91 USA United Kingdom 3.331 4.053 Springer Pergamon-Elsevier Science Ltd 32 29 1.53 1.38 Water Resources Management Ecological Engineering Netherlands Netherlands 2.848 2.914 Springer Elsevier Science BV 27 27 1.29 1.29 Journal of Industrial Ecology USA 4.123 Wiley-Blackwell 25 1.19 Sustainable Cities and Society Environment Development and Sustainability Energy and Buildings Netherlands Netherlands Switzerland 1.777 1.080 4.067 Elsevier Science BV Springer Elsevier Science SA 24 24 24 1.15 1.15 1.15 0.598 3.954 Natl Acad Sciences Elsevier Sci LTD 21 21 1.00 1.00 1.538 Routledge Journals, Taylor & Francis LTD 20 0.96 Transportation Research Record USA Current Opinion in Environmental United Kingdom Sustainability Water International USA Note: Impact Factor (IF) as at the year 2016. 48 2.4.3.1.1 Citation bursts and centrality scores Twenty-four (24) cited journals received citation bursts, out of which 11 journals received citation bursts of 10.0 and above as shown in Table 2.5. Table 2.5: Journals' citation bursts Journals Burst strength Span 47.71 1996 – 2009 37.63 1997 – 2010 Building and Environment 24.59 2007 – 2012 Environmental Science & Technology 23.50 2011 – 2014 Proceedings of the National Academy 23.19 2013 – 2014 21.34 2009 – 2012 Journal of Industrial Ecology 19.08 2013 – 2014 Nature 18.23 2011 – 2014 Journal of Environmental 17.55 1993 – 2008 Landscape and Urban Planning 14.34 2011 – 2012 Sustainable Development 11.11 2013 – 2016 World Commission on Environment and Development [WCED] International Journal of Sustainable Development & World Ecology of Sciences Environmental Impact Assessment Review Management The highlighted journals with citation bursts imply articles in these journals have received strong citations within the specified ‘short’ time span. Hence they are recommended together with the top 20 source journals for researchers in the field to follow. Some nodes received high betweenness centrality scores as identified by purple trims in the network (Figure 2.8). The network revealed source journals such as Ecological Economics (centrality = 0.80), Our Common Future (0.49), International Journal of Sustainable Development and World Ecology (0.32), Environmental Management (0.30), Water Science & Technology (0.26), Nature (0.24), Science(0.23), Energy Policy (0.20), Journal of Cleaner Production (0.13), Ambio (0.13) and Environmental Impact Assessment Review (0.12). These journals serve 49 as links between distinct journals and acts as key intellectual hubs for academics, practitioners and government bodies. Figure 2.8: Journal co-citation network 2.4.3.2 Author co-citation network The author co-citation analysis draws a pattern of relationships among distinct authors whose work appeared as cited references in the same publication. The dataset from the WoS records was used in generating the author co-citation network as shown in Figure 2.9 with 98 nodes and 271 links. Also, the network has a modularity (Q = 0.529) and mean silhouette, S= 0.781. The node size is a representative of the co-citation frequency of each author within the dataset, and the links indicate an indirect cooperative alliance of the authors based on their co-citation frequency. The ten (10) most cited authors were identified from the network, and it is noteworthy that five (5) of the ten most cited authors are international and regional governmental organizations, this finding is a great plus to the global drive for sustainable urban development. These authors include (note: * headquarter of organization): United 50 Nations (frequency = 230, USA*), World Commission on Environment and Development [WCED] (frequency = 209, USA*), World Bank (frequency = 129, USA*), Rodrigo Lozano (frequency = 126, Sweden), Organization for Economic Cooperation and Development [OECD] (frequency = 110, France*), European Commission (frequency = 87, Belgium*), John Elkington (frequency = 54, Australia), Thomas Saaty (frequency = 50, USA), Donella Meadows (frequency = 44, USA) and Robert Yin (frequency = 41, USA). Also, there is affiliation-based diversity among the authors, which lends further credence to the evolution of sustainability research field. One of the authors in the person of Rodrigo Lozano also appeared among the top productive author in the field (Table 2.1) and based on WoS records his article on “Envisioning sustainability three-dimensionally” (Lozano, 2008) has received 117 citations as at the end of 2016. 2.4.3.2.1 Citation bursts and centrality scores Authors with citation bursts with an increase in their articles’ citations within a brief period were identified from the networks. These authors include: WCED (burst strength= 29.67, 1996–2012), European Commission (burst strength= 13.24, 2004– 2018), IPCC (burst strength= 12.85, 2011–2014), UNESCO (burst strength= 12.26, 2013–2014), and Mathis Wackernagel (burst strength= 11.49, 1996–2010), Johan Rockstrom (burst strength= 11.30, 2013–2014). Other authors with citation bursts are Karl-Henrik Robert (burst strength= 11.24, 1998–2008), World Bank (burst strength= 9.77, 2004–2012), David Pearce (burst strength= 9.53, 1993–2000) and Donella Meadows (burst strength= 8.85, 2013–2014). Articles, documents, and communique issued by these authors are worth following, and their works have influenced the development of sustainability research and the idea of the sustainable urban city. Moreover, some nodes with high betweenness centrality were identified from the network (Figure 2.9) as indicated by purple trims. Authors with high betweenness centrality scores are Mathis Wackernagel (centrality = 0.46), WCED (0.22), OECD (0.19), Rodrigo Lozano (0.17), Donella Meadows (0.15), and World Bank (0.15). Other authors with high centrality scores are Gordon Mitchell (centrality = 0.13), Robert Costanza (0.12), Joel Heinen (0.12), European Commission (0.12) and KarlHenrik Robert (0.11). These authors are the influential and pivotal contributions to 51 sustainability research and help connect the different research communities. Zhao (2017) noted that it is an unlikely occurrence for an author to receive a high betweenness centrality score and have high citation count and that in cases of such rare instances then such author(s) have made significant impacts in such field. Figure 2.9: Author co-citation network 2.4.3.3 Document co-citation network Document co-citation analysis evaluates the references cited by the 2094 bibliographic records towards understanding the intellectual structures of sustainability knowledge domain. Citation records from the WoS records reveal that 35 cited documents received a hundred or more citations as at the end of 2016 as shown in Table 2.6. Also, 13 articles (37 percent) of the top 35 top cited articles were published in the Journal of Cleaner Production which was also the source journal with most publication on sustainability topics. Mohanty et al. (2002) who received the highest citations count of 770 citations examined the challenges and opportunities in using natural fibers or its polymers which are based on renewable materials to resolve environmental issues in the industry. The article also advocated production 52 of materials and products from a mix of both renewable and nonrenewable sources and continuous research in that direction. Moreover, Nicol and Humphreys (2002) investigated the theoretical concept of thermal comfort in buildings and recommended several parameters such as the best comfort temperature, indoor temperature and the advocated the need for the development of sustainability criteria for adaptive thermal comfort in facilities. Meanwhile, Kennedy et al. (2007) carried out a comparative analysis of the urban metabolism of eight cities across five continents and discovered an increased metabolism with respect to water, solid waste, energy and air pollutants flow which threatens the sustainability of these cities. They advocated for the development of strategies to reduce its impact on the ecosystem. Table 2.6: Top 35 cited articles based on WoS citation metric S/N Article Article 13 Total citations 168 Total citations 116 S/N Article 25 Liu et al. (2008) 167 26 110 163 27 Troschinetz and Mihelcic (2009) Baddoo (2008) 162 28 107 Mohanty (2002) 2 Nicol and Humphreys (2002) Kennedy et al. (2007) 358 14 311 15 4 Ortiz et al. (2009) 271 16 Luttropp and Lagerstedt (2006) Sophocleous (2000) Glavic and Lukman (2007) Fiksel (2003) 5 Azapagic (2004) 233 17 Lozano (2006) 140 29 6 Robert et al. (2002) 226 18 135 30 7 Folke et al. (1997) 215 19 131 31 Kloepffer (2008) 104 8 Tukker and Tischner (2006) Damtoft et al. (2008) 212 20 125 32 21 122 33 Labuschagne et al. (2005) Cucek et al. (2012) 196 22 120 34 174 23 118 35 Maxwell and van der Vorst (2003) 173 24 Corinaldesi and Moriconi (2009) Makropoulos et al. (2008) Lozano and Huisingh (2011) Shrestha et al. (1996) 103 208 Schneider et al. (2010) Krajnc and Glavic (2005) Mitchell et al. (1995) Brown et al. (2009) Adger et al. (2002) Dovi et al. (2009) Lozano (2008) Krotscheck and Narodoslawsky (1996) Jeon and Amekudzi (2005) Cole (1999) 9 10 11 12 al. S/N 1 3 et Total citations 770 117 A document co-citation network (see Figure 2.10) was generated from the WoS dataset which resulted in 176 nodes, and 549 links and each node represented a cited document and labeled with the name of the first author and the year of publication, while the link signifies the co-citation relationship between two articles. 53 109 106 106 102 100 100 Also, the network has a modularity (Q = 0.741) and mean silhouette, S= 0.538. The node size for each document is a representative of the co-citation frequency of the node article. The node documents in this network (Figure 2.10) are in the distinct set of 74,998 articles cited by the 2094 bibliographic records in this study and may not constitute part of the indexed corpus. The top six (6) co-cited documents with more 30 or more co-citation counts are: WCED (1987) (frequency = 178), Rockström et al. (2009) (frequency = 45), Lozano (2006) (frequency = 39), Gardiner (1995) (frequency = 38), Lozano (2010) (frequency = 30), and Seuring and Müller (2008) (frequency = 30). 2.4.3.3.1 Citation bursts and centrality scores Several documents (19 articles) received citation bursts, of which the top 10 articles with citation bursts were identified and include: Gardiner (1995) (burst strength= 11.12, 2013–2016), Rockström et al. (2009) (burst strength= 10.65, 2011–2012), Robert et al. (2002) (burst strength= 10.07, 2005–2012), Lozano (2010) (burst strength= 9.56, 2013–2016) and Seuring and Müller (2008) (burst strength= 9.56, 2013–2016). Articles such as Lozano (2006) (burst strength= 9.34, 2013–2016), WCED (1987) (burst strength= 9.34, 2005–2012), Robèrt (2000) (burst strength= 6.22, 2005–2011), Barth et al. (2007) (burst strength= 5.70, 2013–2014), and Cortese (2003) (burst strength= 5.49, 2009–2014) received increased citations over a short period. Lozano (2010) research focused on integrating SD studies in curricula of universities and schools and used Cardiff University as a case study, and the findings revealed a more balanced and holistic course delivery. Also, Lozano (2006) presents challenges that could be faced by institutions who decide on integrating SD concepts in their curriculum and highlight ways of resolving such issues. Meanwhile, some documents also have high betweenness centrality scores as denoted by purple trims in the network (Figure 2.10) and these include: WCED (1987) (centrality = 0.70), Elkington (1997) (0.45), Wackernagel et al. (1999) (0.35), Mitchell (1996) (0.28), Meadows et al. (1972) (0.19) and Mitchell et al. (1995) (0.17). These documents are the fundamental bedrock of sustainability research and form the base of most sustainability themes. 54 Figure 2.10: Document co-citation network 2.4.4 Clusters analysis Cluster analysis is an exploratory data mining technique used in this study to identify and analyze the salient terms and context, its trends and their interconnection within the sustainability research field. CiteSpace was used as the tool to get insight into the distribution and structures of the research themes over the years. These terms, themes or context are of distinct classification, and the Log-Likelihood ratio (LLR) was used as the clustering technique due to its ability to generate high-quality clusters with high intra-class similarity and low inter-class similarity. Hence, keywords or terms grouped within a group must be related to one another and different from keywords in other categories. Therefore, cluster analysis facilitates the classification of a large corpus of research data into manageable units and helps to deduce information about each group or cluster objectively. Clusters defined in this study are in two parts: (i) keyword clusters- which are based on the classification of the author keywords and the keywords plus (journal’s indexed terms); and (ii) document co-citation clusters – which are based on keywords in cited references or documents. 55 2.4.4.1 Keywords clusters Nine salient keyword clusters were identified in the clustering of the indexed corpus keywords as defined by the LLR algorithm. The keyword clusters as shown in Table 2.7 are labeled and sorted by size; the cluster size is the number of members in each cluster. Hence, cluster #0 “sustainable development” and #1 “sustainable indicator” with 12 members each are the cluster IDs with the largest group size and cluster #8 “green chemistry” been the smallest sized cluster with two (2) members. Majority of the relationships (as depicted by green links) in clusters #0, #1, #2 and #4 are formed between 2003 – 2006 while some links in clusters #1 and #2 are formed between 2015-2016. The relationships between clusters #3 and #5 (depicted by blue links) are mostly developed in the early days of sustainability research (1993 – 1996). It is evident from the keyword cluster network (Figure 2.11) that recent development in sustainability research has centered around clusters #1 and #2, as shown by the orange and red links. Table 2.7: Keyword clustering of Sustainability research (1991 - 2016) Cluster ID #0 Size Silhouette 12 0.588 #1 12 #2 #3 #4 #5 #6 #7 #8 Cluster label (LLR) Alternative label Mean year 2000 LCA; economic growth 0.749 Sustainable development Sustainability indicator Framework; analytic hierarchy process 2003 11 10 9 0.569 0.617 0.860 Public policy Impact China 2002 1998 2002 6 5 4 2 0.933 0.832 0.774 1.000 Indicator Management Natural capital Green chemistry Research policy; R & D Performance; pollution Water resource; ecological footprint Monitoring; sustainable use Perspective; strategy Decision; cost benefit Metrics; hydrocarbon 1995 2005 1995 2003 The silhouette scores for the clusters ranges from 0.558 to 1.000 which shows the members of the cluster falls well within their group. The silhouette metric according to Rousseeuw (1987) measures and compares the average homogeneity (tightness and separation) of a cluster and could be used to validate a cluster. Meanwhile, the mean year depicts whether the cluster is formed by recent articles or old ones. Clusters #3, #5 and #7 are formed by relatively old articles than other clusters. 56 Figure 2.11: Keyword clusters network 2.4.4.2 Documents co-citation clusters Twenty-one (21) document co-citation clusters were generated from the research power network using the LLR algorithm as shown in Figure 2.12. Meanwhile, only 12 clusters (see Table 2.8) are significant while the other nine (9) clusters have zero silhouette scores and just one (1) cluster member, hence are not counted as salient clusters in sustainability research. The 12 salient and significant clusters are sorted by size as shown in Table 2.8. Cluster #0 “water management” with 38 members is the largest cluster of proportion and while clusters #12 to #20 with just one member are the smallest clusters by size. Most of the relationships in the clusters as depicted by light blue and green links revealed that most of the relationships within the clusters are formed between 1994 and 2001; and this timespan forms the period in which the bedrock of the sustainability research field was laid. 57 Table 2.8: Documents co-citation clusters of Sustainability research (1991 - 2016) Cluster Cluster label Mean Representative Size Silhouette Alternative label ID (LLR) year documents #0 39 0.827 Water Flood protection; 1994 WCED (1987) management hydrological data #1 24 0.852 Higher education University; campus 1999 Lozano (2006) sustainability #2 21 0.758 Perspective Sustainable 2001 Rockström et consumption and al. (2009) production; systemic perspective #3 21 0.951 Cost-benefit Substitution; 1992 Costanza et al. conservation (1998) #4 20 0.992 Sustainable urban Evaluation; 1999 Mitchell (1996) development classification of assessment method #5 19 0.945 Development Environmental 1995 Wackernagel model protection; public and Rees participation (1998) #6 11 0.967 Sustainability Guideline; service 1999 Azapagic and indicator Perdan (2000) #7 3 1.000 Monitoring Modelling; policy 1990 Ten Brink et al. analysis (1991) #8 3 1.000 China Water resources 1998 Loucks (2000); management; urban Simonovic water supply (1996) #9 2 1.000 Environmental Building assessment; 2001 Robinson assessment stakeholder (2004) participation #10 2 1.000 Management Himalaya; India 2002 Berkes et al. (2003) #11 2 1.000 Human ecology Hierarchy theory; 1983 Prigogine and diversity Stengers (1984) The silhouette metric scores for the 12 salient document co-citation clusters ranges from 0.758 to 1.000 which shows relatively higher scores than the keyword clusters and shows that there is consistency within the cluster members. Meanwhile, as regards the clusters’ mean year, most of the clusters are formed by relatively old documents, and this is consistent with the fact that the foundation of sustainability research was formed from the mid-1990 to early 2000s. As shown in Table 2.5, each salient cluster has representative documents which are the journal articles or documents with the most co-citation frequency within each cluster. The representative document influences the labeling of each cluster and are also well cited in the field, hence worth following. 58 Figure 2.12: Document co-citation clusters 2.4.5 Geospatial analysis A geospatial analysis of sustainability research corpus was carried out with the generation of Keyhole Markup Language (KML) files using CiteSpace. These KML files are then converted into animated maps using Google Earth® application which ease its visualization functionality for the location (or origins) of the authors of the study’s indexed sustainability research corpus and highlighting the authors’ published documents from 1991 – 2016 at a specific location. Figure 2.13 shows the geospatial visualization of published sustainability research documents across Europe spanning the period from 1991 – 2016. The red nodes on the map (see Figure 2.13) are the origins of the published works while the lines (of differing colors such as green, yellow, orange, red, pink and purple, etc.) connects the location of documents of the same year. Some of the nodes are a combination of several linked nodes within the same location; this is revealed when such nodes are clicked on the animated map. 59 Figure 2.13: Geospatial visualization of published research documents (Europe) Also, when any of the nodes is clicked, the pop-up dialog is revealed (see Figure 2.14) detailing the documents linked to the specified node. Such information detailed as a link, the name of the first author, year of publication and the journal of the published document. When a specific document link is clicked, the Google Earth® app will redirect the user to the source (web link) of the published document or article. The animated map is handy for academics and practitioners, as a more dynamic alternative to scientific databases such as Scopus or ISI Web of Science in the quest to ease the identification of sustainability research publications within a city or region. Hence, using this study animated map would be useful in tracking the trend of articles published over the years in the various countries. The dataset for the geospatial map (including the dynamic geospatial map and the KML files) is accessible as published via Mendeley data, DOI: 10.17632/sv23pvr252.1 (Olawumi & Chan, 2017). 60 Figure 2.14: Geospatial visualization of published research documents (showing part of China) 2.5 Identification of the salient research clusters The salient clusters in sustainability research field as shown in Table 2.8 are cluster #0 to cluster #11, however, to conserve space, the review centered on seven (7) clusters (clusters #0 to cluster #6) with a minimum of 11 cluster members. Cluster #0 “water management” has 39 members and the representative document is a communique published by the United Nations (WCED, 1987) which detail the opinions, reflections of the Brundtland conference on environment and SD. The report gave the first definition of sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (WCED, 1987). It further for an actualization of SD, there must be the identification of needs and the limitations that might hinder the capacity to meet such needs. The report by WCED (1987) also highlighted challenges faced in the realization of sustainable urban development which includes issues related to energy, industrial growth, the ecosystem, urban problem in developing countries and resource base (such as water management, land use, human resources, technological support) among others. Meanwhile, a study by Holden et al. (2014) shows no country has achieved the four thresholds of sustainable development as identified in the Brundtland report, with 61 many nations far off the minimum target. Accordingly, they argued for the integration of technology and behavioral changes of stakeholders for the actualization of sustainable urban development by 2030. Cluster #1 “higher education” had 24 members with Lozano (2006) has the representative document for the cluster. Lozano (2006) work focused on how the SD concepts proposed in the Brundtland report can be integrated into universities and colleges. Accordingly, Lozano (2006) highlighted the possible resistance the idea of institutionalizing SD could face from the stakeholders and presented strategies to overcome these challenges to integrate the SD ideas and concepts in universities' policies, system, and activities and ensure campus sustainability. Cluster #2 “perspective” had 21 members with Rockström et al. (2009) has the representative document for the cluster. Rockström et al. (2009) proposed a novel approach to serve as guideline or preconditions for current and future urban development. They argued that defining boundaries for human development which help to prevent catastrophic environmental changes and ensure the stability of the built environment. Cluster #3 “cost-benefit” also had 21 members with the representative document published by Costanza et al. (1998). Costanza et al. (1998) attempted to make an analogy between the ecosystem functions and ecosystem services and argued that they contribute the social well-being of humans as well as represent a significant part of the economic value of the planet earth. The article also highlighted various valuation method to estimate the ecosystem services and recommended the need to safeguard the scarce ecosystem services to prevent its misuse. Cluster #4 “sustainable urban development” had 20 members with the representative document published by Mitchell (1996). Mitchell (1996) outlined the challenges and limitations faced in the application of SD index and the various sustainability principles which have been hindering the implementation and promotion of SD at the local level. Also, Mitchell (1996) noted that there is no specific measurement tool for assessing SD. Although some building rating systems such as LEED, BREEAM, BEAM Plus and others have been developed since then; yet these tools focused mainly on some aspect environmental sustainability with gaps to be filled in areas such as social and economic sustainability constructs of SD. 62 Cluster #5 “development model” had 19 members with the representative document published by Wackernagel and Rees (1998). Wackernagel and Rees (1998) relayed the need for humans to reduce its ecological impacts on the environment and categorized the challenge being faced in achieving it, as that that has more to do with human’s social behavior than a technical or environmental crisis. A planning model was proposed by Wackernagel and Rees (1998) to serve as a tool for the measuring humans’ ecological footprints. Cluster #6 “sustainability indicator” had 11 members with the representative document published Azapagic and Perdan (2000). Azapagic and Perdan (2000) proposed a framework featuring sustainability indicators that cover the three pillars of SD- social, economic and environmental sustainability. Although the sustainability indicators (SI) were designed for its application for the whole industry, it would be more useful and functional when refined to specific sectors of the built environment. The SI can only be implementable when its users or stakeholders adopt appropriate strategies by evaluating alternative options. One of such multi-criteria decision-making technique is the Analytic Hierarchy Process (AHP) or the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). 2.6 Conclusions and future directions The concept of sustainability and sustainability development have received increasing global attention and consideration from government agencies, academics, practitioners and international organizations. It has evolved from its concept statements stated in the 1987 Brundtland commission report to the integration of technological tools to enhance its implementation. This study provides a scientific visualization method to analyze 2094 WoS bibliographic records using scientometric techniques such as co-author analysis, co-word analysis, co-citation analysis, clusters analysis, and geospatial analysis. These methods were used to an in-depth understanding of the status quo and trend in sustainability research field. An analysis of the research publication trend revealed a steady increase in the number of the bibliographic records of the years which shows more efforts and resources are devoted to sustainable urban development. Also, as regards general productivity and contribution among authors, findings revealed Donald Huisingh, Rodrigo Lozano, and Yong Geng as the top three lead authors in the field. These 63 authors along with Tsuyoshi Fujita, Robert Axelsson, and Per Angelstam are the central authors within their research circuits. However, only Donald Huisingh and John Cairns received high citation bursts over a short period, although John Cairns does not have many publications. Meanwhile, in terms of distribution of the publications on sustainability, the majority of the journal articles originated from the United States, China, United Kingdom, and Canada. The Chinese Academy of Sciences, China PR, Delft University of Technology, Netherlands, and the University of British Columbia, Canada are the most productive institutions in sustainability research projects. Also, the diversity of the highly cited authors from various regions and organizations reveals the evolution of sustainability research and demonstrates its widely flourishing acceptance. Also, there are some active and connected exchange platforms between the countries and institutions. Furthermore, key subject categories such as “environmental sciences,” “green & sustainable science technology,” “environmental engineering,” and “civil engineering” have had considerable influence on the structure and development of sustainability research and help to connect the distinct aspects and concepts in the research field. In terms of keywords, “sustainability”, “sustainable development”, “management”, “system”, “indicator”, and “framework” had the most frequency; while “impact”, “environment”, “climate change”, “design”, “policy”, “city” and “energy” received the citation bursts in recent years (2012 to date). It is consistent with the fact that more efforts are devoted to these critical research themes in the past years which are pivotal in achieving a sustainable urban development. The core and high impact journals such as Journal of Cleaner Production, Sustainability, International Journal of Sustainable Development & World Ecology, Sustainability Science, Ambio, and Water Science & Technology have published significant findings in sustainability research. Some of these journals also received co-citation frequency and high citation bursts in the past years and a considerable number of the 35 highly cited articles are published in these journals. Also, the top 20 source journals have a minimum of 1.00 impact factor. The document co-citation analysis reveals Mohanty et al. (2002), and Nicol and Humphreys (2002) have the most cited documents while publications by the Brundtland Commission, WCED (1987) received the highest co-citation frequency along with documents such as 64 Rockström et al. (2009) and Lozano (2006). Meanwhile, documents such as Gardiner (1995), Seuring and Müller (2008), Lozano (2006), Barth et al. (2007) and Cortese (2003) received high citation bursts in recent years (2014 – 2016). Cluster analysis was used in this study to analyze and conceptualize the salient terms and context of sustainability research using two approaches of keyword and document co-citation clusters. Nine (9) keyword clusters and twenty-one (21) document co-citation clusters were identified based on the indexed research corpus. These emerging trends and hot-topics related to sustainability research can be summarized as sustainable urban development, sustainable indicators and impact, water management, environmental assessment, strategy, public policy and monitoring, cost-benefit analysis, stakeholders’ participation, campus sustainability and human ecology. The discussion section on the salient research clusters reveals the evolution of sustainability research field from the definition of its concepts in the Brundtland Commission report to the recent development of models and sustainability indicators to enhance the actualization of sustainable urban development. Moreover, a geospatial analysis and visualizations of the research corpus produced a useful and dynamic animated map to improve the ease of identifying the sustainability researchers’ origin and highlighting the authors’ published documents for a specified year and region. The study provided valuable information to researchers, practitioners and governmental bodies in the field of sustainability research. The power research networks offered valuable insight and in-depth understanding of the key scholars, institutions, state of the research field, emerging trends, salient topics and an animated map for researchers. Also, the study helped to crystallize out information and key findings to enhance the implementation of a holistic sustainability to achieve SD. It also identified the key authors and institutions who they can consult to assist in developing sustainability policies or templates for their applications. The scientometric analysis and visualization had helped to reflect the global picture of sustainability research accurately, and these tools could be useful to visualize the emerging trends in other research fields. Meanwhile, it is recommended for researchers to focus more attention on the emerging sustainability research themes 65 such as ecological footprint, LCA, sustainability assessment model, policy analysis and monitoring, evaluation metrics, stakeholder participation. The findings will be applicable to (1) government agencies and corporate organizations in their policy formulation and consultation as well as partnering with the key institutions identified in the study, (2) graduate students in identifying gaps and progresses made in the sustainability research area (3) academics in networking with other researchers in their areas of specializations (4) industries or sectors such as the construction industry in identifying and enhancing their level of implementation of sustainability to achieve a sustainable smart city initiative. Future studies on sustainability research themes may focus on the application or integration of innovative technologies such as BIM, augmented reality, radiofrequency identification (RFID), geographical information system (GIS) among others to enhance the sustainability of the built environment towards the achievement of sustainable smart cities. Other aspects for future research may center on the application of sustainability knowledge in waste management, reduction of carbon footprint, campus sustainability, green neighborhoods as well as developing countryspecific sustainability evaluation index. 2.7 Chapter Summary The concept of sustainable development has gained worldwide attention in recent years which had enhanced its implementation. However, few studies have attempted to map the global research of sustainability. This chapter presented the scientometric review of global trend and structure of sustainability research in 1991 – 2016 using techniques such as co-author, co-word, co-citation, clusters, and geospatial analyses. A total of 2094 bibliographic records from the Web of Science database were analyzed to generate the study’s research power networks and geospatial map. The findings reveal an evolution of the research field from the definition of its concepts in the Brundtland Commission report to the recent development of models and sustainability indicators. The most significant contributions in sustainability research have originated primarily from the United States, China, United Kingdom and Canada. Also, existing studies in sustainability research focus mainly on subject categories of environmental sciences, green & sustainable science technology, civil engineering, and construction & building 66 technology. Emerging trends in sustainability research were sustainable urban development, sustainability indicators, water management, environmental assessment, public policy, etc., while the study generated 21 co-citation clusters. This chapter provides its readers with an extensive understanding of the salient research themes, trends and pattern of sustainability research worldwide. The following chapter reviews the extant literature and practice on the intellectual structure of BIM and the current trends and application of BIM in the built environment. 67 CHAPTER 3: EVOLUTION IN THE INTELLECTUAL STRUCTURE OF BIM RESEARCH: A BIBLIOMETRIC ANALYSIS3 3.1 Chapter Overview The previous chapter conducted a comprehensive review and scientometric analysis of the concept of sustainability and sustainable development as it relates to the built environment. The current chapter presents the comprehensive reviews and bibliometric analysis of 445 corpus data of BIM literature towards investigating and understanding the pattern of BIM research. Network maps that display the visualization of the structure of BIM are also presented. This chapter will also provide relevant research areas that require considerations, and the discussion of selected research areas provide an extensive understanding of salient BIM fields. 3.2 Introduction The development and integration of information technologies (IT) have helped improved construction processes and practices in the built environment (Aksamija, 2012; Chien & Mahdavi, 2009; Dawood, 2009; Thomassen, 2011; Wikforss & Löfgren, 2007). Thus, the construction industry been as a composite sector, made up of diverse stakeholders (Olatunji et al., 2016b; Olawumi et al., 2016; Olawumi & Ayegun, 2016) need to be proactive in its adoption of IT in its operation. Buswell et al. (2007) noted that a construction project has traditionally relied on 2D drawings to convey project information and data. However, the advent and increasing importance of Building Information Modelling (BIM) have changed the working system in the industry with the development of 3D models of building structures, and its capacity to integrate other concepts such as sustainability, project scheduling, costing and facility management. This chapter is fully published in: Olawumi, T.O., Chan, D.W.M., & Wong, J.K.W. (2017). Evolution in the Intellectual Structure of BIM Research: A Bibliometric Analysis. Journal of Civil Engineering and Management, 23(8), 1060–1081. https://doi.org/10.3846/13923730.2017.1374301 And partially in: Olawumi, T.O., & Chan, D.W.M. (2019b). Building Information Modelling and Project Information Management Framework for Construction Projects. Journal of Civil Engineering and Management, 25(1), 53–75. https://doi.org/10.3846/jcem.2019.7841 Olawumi, T.O., & Chan, D.W.M. (2019e). Development of a Benchmarking Model for BIM Implementation in Developing Countries. Benchmarking: An International Journal, 26(4), 1210–1232. https://doi.org/10.1108/BIJ-05-2018-0138 3 68 BIM is currently receiving worldwide recognition in the AEC industry due to its ability to store and also ease the use and reuse of project data across the project development phases; while also preventing unnecessary replication of project or design tasks (Kovacic et al. 2015; Kim et al. (2013b); Lee & Yu 2016; Sun & Wang 2015). However, BIM is a revolving and innovative digital technology (Mahalingam et al. 2015; Malekitabar et al. 2016; Succar & Kassem 2015); with recent applications in areas such as sustainability (Ajayi et al., 2015; Liu et al., 2015b) and facility management (Motamedi et al. 2013); despite the fragmented nature of the construction industry. Meanwhile, BIM has started to receive more attention in the academic community with several research papers on BIM. It include articles on the development of BIM curriculum for university undergraduate students. Related works on BIM curriculum development including literature such as “course development and collaborative teaching” (see Ahn et al., 2013; Becerik-Gerber et al., 2012b; Becerik-gerber & Kensek, 2010; Kim, 2012; Sacks & Barak, 2010; Wang & Leite, 2014; Wu & Issa, 2014a). It also includes “evaluating BIM curriculum vis-à-vis industrial needs” (Aibinu & Venkatesh 2014; Solnosky et al. 2014) and “in-class experimentation with BIM tools” (Lewis et al. 2015; Nassar 2012) among many other related topics. BIM per Eastman et al. (2008) is “a new approach to design, construction, and facilities management, in which a digital representation of the building process [is used] to facilitate the exchange and interoperability of information in digital format.” Gilkinson et al. (2015) regarded it as both constituting a process and technology. It is an innovative solution with much to be explored; they further stressed that since it ensured a “coordinated integrated process”; it is a useful tool for all project stakeholders as they could found it fit and suitable for their jobs due to its diverse nature (Olawumi 2016a), and the collaborative outlook of the construction industry (Olatunji et al., 2016a, 2016b). Olatunji et al. (2017b) listed one of the benefits of BIM as an increase in return on investment (ROI) for clients; and to “facilitate the ease of dissemination of information” and this, in turn, helps to secure project success (Olawumi 2016b; Olawumi et al. 2016). Also, related technological tools such as augmented reality (AR) system and Geographic Information System (GIS) have also been integrated into the BIM process to facilitate the visualization of the construction process. 69 More so, BIM is a repository of digital information which eases the management of information in a project. Abanda et al. (2015) depicts BIM as a “global digital technology” with the capacity to ease the construction process, facilitate coordination and enhance the efficient delivery of project information. Also, Sampaio (2015) described BIM as an “innovative technology” which can support project activities throughout the project lifecycle. Moreover, Eastman et al. (2008) defined it as a “modeling technology and associated set of processes to produce, communicate, and analyze building models.” More so, Zhao (2017) noted that BIM had transformed the construction industry in such a way that construction stakeholders have developed an interest in its implementation for their diverse job nature (Olatunji et al., 2016b; Olawumi et al., 2016; Olawumi & Ayegun, 2016). Mccuen (2008) advocated that BIM provide “single, non-redundant, interoperable information repository” capable of supporting every stage, process and functional units in a construction project. Tulubas Gokuc and Arditi (2017) described BIM as a “trend of the future” with significant impacts on professional performances. One of the benefits of BIM implementation in projects is to facilitate effective communication among project stakeholders (Olatunji et al., 2017b) and could enhance business operations (Ahankoob et al., 2018). Consequently, BIM has seen a dramatic increase in its application across the project phases in recent years (Dim et al., 2015; Patrick & Ii, 2010). According to Eastman et al. (2008), BIM is one of the several other technologies in the architecture, engineering, and construction (AEC) industries and per Autodesk (2010b), BIM is a more sophisticated tool with better feedback mechanisms for its users. Also, according to Olawumi and Chan (2018d), BIM as a versatile technology can help advance the implementation of green buildings and innovations in the built environment. In contrast, some of the existing IT applications in the construction industry are constrained by their reliance on static methods of information delivery (Aziz et al., 2009; Buswell et al., 2007). Olawumi and Chan (2018b) identified BIM as one of the recent innovative concepts in the country industry which per Olawumi and Chan (2018d) has improved the design and construction of building projects. Inyim et al. (2015) described BIM as “an advanced example of an ICT approach” and outlined ways in which the construction industry uses ICT: (1) information management and service; (2) communications; 70 and (3) processing and computing. Pero et al. (2015) pointed out the usefulness of BIM as a veritable IT tool for data sharing and exchange. BIM is a process of generating and managing information of a building or infrastructure during its life cycle (Kuiper & Holzer, 2013). Eastman et al. (2011) also defined it to be “a modeling technology and associated set of processes to produce, communicate, and analyze building models.” Nevertheless, per Van Lith et al. (2015), the definition of BIM is still under debate with several interpretation depending on how it is deployed in a project. Meanwhile, Olawumi et al. (2018) categorize BIM implementation in two aspects such as (1) the BIM product or technology uses in creating building models and simulating the design parameters, and (2) the BIM process which affords the synthesis of relevant of information relating to a project within a central hub. However, according to Matthews et al. (2018), the inadequate knowledge and experience on how to adopt BIM in a project as resulted in stakeholders undertaking its implementation in a cluttering manner. In recent years, several research studies have been conducted on the impact of BIM implementation in the Architectural, Engineering, and Construction (AEC) industry. extant literature such as Bradley et al. (2016) outlined the benefits of BIM in infrastructural projects while Fan et al. (2014) using a case study project examined the influence of BIM during the construction phase. The case study’s findings reveal a significant reduction in change orders, requests for information (RFI) and a better compliance to project schedule. Also, a study by Johansson et al. (2015) pointed out the capacity of BIM to facilitate large projects by providing visualization and real-time rendering of the projects while Karan and Irizarry (2015) argued that extending BIM capacity using tools such as Geographical Information System (GIS) can enhance its efficiency at the preconstruction stage. Moreover, Inyim et al. (2015) highlighted areas in which BIM has been of benefit to the construction industry to include information management and service, communications. Studies (see Kim et al., 2015c, 2016c; Matthews et al., 2015; Morlhon et al., 2014; Neto et al., 2016; Oti et al., 2016; Olatunji et al. 2017b) also revealed the significant advantages gained in project information management through the implementation of BIM to include (1) compliance to project’s delivery schedule; (2) resource planning and management; (3) faciltate collaboration among key stakeholders; and (4) real-time simulation and analysis of building performance 71 among others. Extant literature (see Kang & Hong, 2015; Pärn & Edwards, 2017; Park & Cai, 2017) also examined the influence of BIM at the facility management stage of the building lifecycle. Also, some empirical studies (see Ham & GolparvarFard, 2015; Ilhan & Yaman, 2016; Kim et al., 2015a, 2015b, 2015c) argued for the enrichment of BIM to aid sustainable development. Demian and Walters (2014) highlighted the use of BIM to manage project information management in construction projects and stressed that the adoption of BIM in the construction industry has helped bring solutions to the sector’s problems. Fisher and Yin (1992) dated the utilization of Information Technology (IT) in the United Kingdom to early 1970s and further opined that the globalization of construction works such as the pre-fabrication and assembly of building components will greatly increase the usefulness of IT in construction projects. The prediction of Fisher and Yin (1992) is a current reality in the construction industry as evidenced in some construction projects (Bansal, 2011a; Davies & Harty, 2013). Moreover, Olawumi et al. (2017) and Olawumi and Chan (2018b) argued that for the construction industry to strive and be competitive, it needs to be innovative and improve the ways, methods, and techniques of delivering its products. 3.2.1 Knowledge Gap and Research Objectives and Value Previous studies on reviews of BIM literature have focused on specific research areas or themes such as facility management (Becerik-Gerber et al., 2012a; Kang & Hong, 2015; Wetzel & Thabet, 2015), environmental sustainability (Wong & Zhou 2015). Studies have outlined the current practices and future directions via various research approaches such as surveys, critical literature reviews, and interviews (Azhar 2011; Gu & London 2010; Volk et al. 2014). More so, researchers have carried out reviews and analyses on BIM which include contractors’ blueprint to adopt BIM (Ahn et al., 2016); e-tendering process model (Ajam et al. 2010); waste management (Ajayi et al. 2015; Akinade et al. 2015); education and knowledge (Ahn et al. 2013; Alci & Sampaio 2015). Others include: social network simulation, cloudBIM and technology adoptions (Al Hattab & Hamzeh, 2015; Alreshidi et al., 2016; Arayici et al., 2011; Becerik-Gerber et al., 2012b; Becerik-gerber & Kensek, 2010; Chen & Hou, 2014; Choi & Kim, 2015; Davies & Harty, 2013; Du et al., 2014). 72 Extant studies also exist on BIM-GIS integration (Bansal, 2011b; Borrmann et al., 2015; Deng et al., 2016) and sustainability (Bynum et al. 2013; Henry et al. 2015; Inyim et al. 2015). However, in recent years (2015 – 2017) there have been literature reviews (see Table 3.1 & Table 3.2) on BIM research field such as Yalcinkaya and Singh (2015), who reviewed BIM literature to deduce twelve (12) BIM core research areas and ninety (90) factor labels using Latent Semantic Analysis (LSA) described as a “natural language processing technique”; which was used to analyze the abstracts of the journal articles. Meanwhile, Zhao (2017) used a computer software “Citespace” to examine citation records downloaded from the Web of Science database to identify authors with the most citations and co-citations and the “hot topics” in BIM research areas with the most citations. More so, Santos et al. (2017) review of the extant literature focused on identifying research areas with the most citations and the most cited authors. Accordingly, he also proposed nine (9) research areas in BIM field. Table 3.1 and Table 3.2 identifies and summarize what is new in this current study and compares it with previous published reviews of BIM literature. Moreover, previous BIM reviews focused mostly on authors and journals’ citations analyses, this study attempts to bridge the gap in the reviews of extant literature and add value to BIM knowledge area. This study proposes the following objectives: (1) to carry out an holistic review of BIM journal articles (as against abstracts’ review by Yalcinkaya and Singh (2015) and citations records by Zhao (2017)); (2) to define the subfields that constitute the intellectual structure of BIM research fields (core research categories and areas); (3) to identify funding (grants) structure for BIM research based on country (research origin) analysis and research category analysis; (4) to identify and establish the network of BIM publication by research origin and geographical scope; (5) to identify the salient research methodology employed in past BIM studies; (6) to identify relevant BIM software, data schema, and project areas for BIM application; and (7) to classify BIM publications based on project sectors they are applied to (such as energy, transportation, etc.). Meanwhile, throughout the bibliometric analysis and the literature reviews, the study would adopt a more systematical and analytical approach in achieving the seven (7) objectives of this study. A wide range of publication will be analyzed across several journal publication houses. The next section focuses on a discussion of the research methodology applied (bibliometric analysis) and the literature search strategy. Other 73 sections focus on (i) the findings and discussion of the results of the bibliometric analysis, (ii) discussion on the proposed core BIM research categories and areas, (iii) the research implications (iv) and research limitations and (v) the conclusion and future directions. 3.3 Research Methodology The study adopted a bibliometric analysis technique to achieve the predefined research objectives of which is to articulate the distinct set of the main research categories in BIM’s research to gain a better perspective and identify critical areas in which more research efforts is still been required. Per Marsilio et al. (2016) bibliometric research approach is an “attempt(s) to quantify and address the intellectual structure of a research field starting from the mathematical and statistical analysis of patterns that appear in the publication and use of documents.” Meanwhile, this analysis technique has been utilized in some research publications both in the science and management research fields (see Neto et al. 2016; Marsilio et al. 2016; Ramos-Rodriguez & Ruiz-Navarro 2004). This research aims to bridge the gap in the BIM literature by applying the bibliometric technique to a corpus of published articles relevant to this disciplinary field towards achieving the predefined objectives as stated in the previous section. The research design for this study is as outlined in Figure 3.1. 3.3.1 Literature Search Strategy In commencing the bibliometric analysis, a decision as to which scientific repository to use was made; there are several academic digital databases, but the three most commonly utilized for scientific inquiries include Google Scholar, ISI Web of Science and Scopus. Although there is no clear difference in Scopus and Web of Science databases as it pertains to science-based publications, however, there is a considerable overlap in their records. Moreover, Google Scholar seems to have a more extensive collection of publications than both Scopus and Web of Science. However, Google Scholar is noted to contain many incorrect publication attributionsas an author with the same first initial and last name may be attributed more citations. Also, Google Scholar also has another minor problem with indexing of articles, as it counts all conference abstracts which has nothing to do with citations, thereby increasing the numbers of papers very dramatically. 74 However, for this study, a comprehensive search as detailed in Figure 3.2 was carried using the ISI Web of Science database because of its “comprehensiveness, organized structure and scientific robustness” (Neto et al. 2016). Marsilio et al. (2011) also argued that it is the “most commonly used and generally accepted source for bibliometric studies.” The search keywords are: “Building Information Modelling,” “BIM” and “Building Information Modeling.” The selected time span is between 1990 and 2016 equivalent to 26 years. The research corpus only comprises of articles published in a journal instead of a doctoral thesis (since most of them are afterward published in journals), books or conference papers. Moreover, authors do publish their work in scholarly journals because they are classified as “certified knowledge” (Ramos-Rodríguez & RuízNavarro 2004) and have gone through a peer-review process. The search results gave 567 journal articles. However, 122 papers were excluded from further analysis as they fail to meet the inclusion criteria (see Table 3.3) due to several reasons such as abstracts written in a language other than English (12 articles). We also have three articles with no abstracts and 107 articles found to be unrelated to research (such as papers that only refer to BIM but cannot be related to any of the categorized research areas). A total 445 articles which were downloaded in Mendeley Desktop (reference manager) and indexed in the Microsoft Excel program. The indexed articles consisted of 316 case studies papers, 47 surveys/interviews articles, and 82 articles which utilized mixed method research approach. Figure 3.1: Outline of Research Design 75 Table 3.1: Summary of Recent BIM Literature Reviews Paper Wong and Zhou (2015) Yalcinkaya and Singh (2015) Zhao (2017) Number of journals analyzed Number of papers analyzed Period of analysis 9 84 2004 – 2014 Proposed research categories (research areas) 4 (none) 2004 - 2014 12 (90) BIM Latent Semantic Abstracts Analysis (LSA) 2005 - 2016 - BIM Scientometric review Only Top 20 • journal listed • - 525 (journal articles) 450 (conference papers) 614 Santos et al. (2017) 49 381 2005 -2015 9 Current study 62 445 2006 - 2016 10 (107) Research areas • • Green-BIM Sustainability BIM 76 • • Type of literature review No bibliometric Content analysis Bibliometric BIM Review of analysis sustainability studies Scope/Focus of analysis Each journal sections paper’ Citation records only • • • • Co-author analysis Co-word analysis Co-citation analysis Co-occurring keywords Each journal sections paper’ Each journal sections paper’ Table 3.2: Comparison between previous BIM Literature Reviews' Findings and the Current study Paper Database used Contribution to Existing Knowledge Wong and Zhou (2015) Scopus • • • Provided a summary of research focus of green-BIM publications Identified the low utilization of BIM in facility management phase Proposed a ‘one-stop-shop’ BIM for environmental sustainability monitoring Yalcinkaya and Singh Several databases like Elsevier, • • Identified 12 core BIM research areas Identified 90 BIM factor labels or keywords. • • • Identified ten (10) co-citations clusters Identified 7 hot topics in BIM research Identified 5 research areas with most citations unlisted databases • • • Proposed 9 research categories Identified research areas and journals with most citations Identified current trends in BIM Web of Science • Identify funding (grants) structure for BIM research (country analysis and research category analysis) Identify the salient research methodology employed in past BIM researches Identify the network of BIM publication by research origin and geographical scope Propose a much concise and precise core research category (10) and areas (107) Identify relevant BIM software, data schema, and project areas for BIM application Classify BIM publications based on project sectors they focused on (such as energy, transportation, etc.) (2015) ProQuest, Emerald, EBSCO, etc. Zhao (2017) Web of Science Santos et al. (2017) Web Current study of Science and other • • • • • 77 Table 3.3: Indexed Corpus Profile Number of Papers Percentage (%) Total publications in web of science 567 100 Abstracts which are written in languages other than English 12 2.12 Papers excluded for not having abstracts 3 0.53 Papers excluded for being registered twice 0 0 Papers excluded for not being related to the research area/topic 107 18.87 Total papers excluded before analysis 122 21.52 Total papers to be analyzed for this guided study research 445 78.48 Profile Figure 3.2: Literature search & indexing approach 78 3.4 Bibliometric Analysis This section discusses the facets and results of this study of bibliometric analysis. These include (i) descriptive analysis of published research in BIM (ii) defining the BIM subfields and research areas (iii) BIM funding structure, and (iv) the anatomy of the BIM publications as outlined in this study research design. 3.4.1 Published Research in BIM The volume of published research in BIM has notably increased in recent years (Santos et al. 2017); since it has emerged as a key, and innovative approach to construction and civil engineering (Yalcinkaya & Singh 2015) and these studies covers diverse areas including several technical and non-technical issues (Zhao 2017). More so, in the last four years, more than 75 percent of BIM articles were published. The literature used in this bibliometric analysis are journal articles, and all but one of the journals has no impact factor (IF). The analysis of the journals reveals that 36 out of the 62 journals (58 per cent) has an impact factor (IF) greater than 1.000 while 16 journals have IF value between 0.500 and 0.999 representing 26 per cent and the rest of 9 journals are below 0.500 IF value. Meanwhile, an analysis of BIM publishing journals reveals several concepts and issues revolving around the BIM research field; also 32 out of the 62 journals (52 per cent) published just one article. More so, another cluster of 20 journals published between 2 and 6 papers; meanwhile, there is another group of ten (10) journals whose number of published articles on BIM is 10 articles or more (see Table 3.4). The structure of the published research on BIM closely followed the “Paretoprinciple” also known as the 80/20 rule (Brynjolfsson et al. 2011; Pareto 1964). In this study, it follows that 349 articles (78.4 percent) are published in just 10 journals (16 per cent) which can be termed a 78/16 rule which aligns with the Pareto postulation; the average impact factor for the 10 journals whose publications followed the Pareto principles is 1.568. More so, from year 2012 to date the number of published articles on BIM have significantly increased with more volumes of articles coming from journals such as “Automation in Construction”, “Advanced Engineering Informatics”, and “Journal of Computing in Civil Engineering”. 79 3.4.2 BIM Sub-fields and Research Areas The next stage in the bibliometric analysis is to define the BIM sub-fields (research categories and research areas). We identify ten (10) research categories namely “ITenabled simulations and visualization”; “building design and energy conformance”; “BIM software & data schema”; “BIM model development”; “BIM learning, adoption & practice”; “construction and project management”; “safety and risk management”; “facility management”; “sustainability-related studies”; and “literature review” (see Table 3.5). The establishment of the semantic link and classification of the published works to the ten (10) research categories was based on a directed content analysis approach using articles keywords, titles, scope covered and the research findings. Meanwhile, further clustering of the articles enables us to define one hundred and ten (107) research areas/themes based on the ten research categories. The analysis of the research categories reveals a prevalent of publication in aspects such as “construction and project management” with 78 articles; “BIM learning, adoption & practice” with 70 articles; and “building design and energy conformance” with 65 articles which sums up close to fifty (50) percent of all published works in BIM. The defined category in this study is more concise and specific than previous studies, although there is a partial overlap when compared with those provided by previous authors (Yalcinkaya & Singh 2015; Zhao 2017). Furthermore, for the research areas, one hundred and ten (107) themes were identified (see Table 3.6) through an iterative process whereby identified themes are grouped in a cluster under each research category. After the loading of the research themes into the research categories, three research areas- “construction and project management” with 18 themes, “facility management” with 17 themes and “BIM model development” with 16 themes are the most represented research areas. It can be deduced from the analysis that BIM has found more application in research in construction and project management with more published articles and themes, this is because of diverse use in the construction industry (Zhao 2017); and the fact that more contractor has started making use of BIM (Fan et al., 2014). Further discussion on the research categories and areas are outlined in section 5 of this chapter. 80 3.4.3 BIM Publications’ Funding Structure BIM disciplinary field is a technologically backed research area which requires researchers to experiment with, develop and interoperate various BIM software and tools of which many of them are only commercially available and expensive, hence this section attempts to investigate the funding arrangement used in the various journal articles. The bibliometric analysis in this section focuses on reviewing the funding structure (i) based on research category analysis, and (ii) research origin analysis (author & coauthors’ affiliations). An analysis of the funding structure reveals a steady increase in the number of BIM research receiving some form of funding or grants to carry out the relevant studies (from 20 articles in 2011 to 67 articles in 2015); also, more than half (51 percent, #231 articles) received funding. Meanwhile, based on an analysis of the research categories which received more funding, we identified four (4) research categories which have more than thirty (30) funded articles, and these include: RC2“Building design and Energy conformance” which 40 funded articles, RC6“Construction and Project Management.” Others are RC3- “BIM Software & Data schema,” and RC4- “BIM model development” with 39, 35 and 32 funded articles respectively which represent more than 60 percent of the funded articles of all BIM research categories (see Table 3.7). More so, the analysis of the funding structure of BIM publication (based on research origin analysis) was analyzed using a mapped network visualization technique as shown in Figure 3.3. The funding arrangement for any given article was based on the information provided by the author(s) in the acknowledgment section of the published paper. The format for the map network visualization analysis in Figure 3.3 is based on “Country (‘articles per research origin,' ‘funded articles,' ‘articles per geographical scope’).” Therefore, by “articles per research origin” we imply the number of articles published by the author(s) or co-author(s) affiliated with an institution based in that country. More so, the term “articles per geographical scope” implies the number of articles with originating data or case studies based in that country. A bibliometric analysis of public-private partnership (PPP) by Neto et al. (2016) also used the terms “research origin” and “geographical scope” to portray similar expressions. 81 For example, we have USA (147,45,118) - this imply that they are 147 BIM authors/co-author(s) from the United States based on the bibliometric analysis and 45 of their BIM articles were funded, while there are 118 articles with the research data (that is, case studies, questionnaire surveys or interviews, etc.) based solely in the United States. BIM articles from countries namely the Republic of Korea, the US and China, have more funded articles with 68, 45 and 17 articles than any other countries which represent a combined 56 percent of all funded articles. The funding of BIM projects in South Korea, and China has aided the adoption and implementation of BIM in the AEC industries of these two countries; also, BIM standards, guidelines and component families which are essential for successful BIM implementation have been developed in Korea (G-SEED) and Hong Kong (BEAMPlus). Figure 3.3: Map network visualization analysis (funding structure) The relative high funding rate from the Republic of Korea is not far-fetched because as noted by Lee and Yu (2016), the Korean central government have strongly 82 encouraged BIM usage and adoption via the promulgation of policies and funding through the National Research Foundation of Korea (NRF). Also, a recent study by Won et al. (2013) revealed a significant increase in the adoption rate in both USA and Korea. 3.4.4 Anatomy of BIM Publications This section focuses on other aspects of the anatomy of the published BIM articles as depicted in the research design. The bibliometric analysis in this section focuses on (i) project sector analysis (ii) the salient research methodologies (iii) related BIM software and data schema. 3.4.4.1 Project Sector Analysis A bibliometric analysis of the corpus of BIM publications reveal a greater focus on the ‘building and housing sector’ of the built environment with a total of 347 BIM articles out of the overall 445 articles. BIM articles in the “building and housing” sector focuses on aspects such as “single family houses”, “residential floors”, “parking garage”, “storey buildings”, “high-rise structures”, “building component and elements”, “sports centers”, and “educational buildings” (see Table 3.8). A study by Chang and Lin (2016) reveals that BIM “is currently being applied mostly to buildings” with few applications elsewhere. Table 3.8 outlines the project sectors covered by the BIM publication corpus as deduced through this study’s bibliometric analysis; meanwhile, aspects covered by the articles under each project sectors were defined. 3.4.4.2 Salient Research Methodologies The bibliometric analysis of the articles’ corpus in this section focus on defining the primary research design and approaches utilized by previous BIM authors. An analysis of the articles reveals that 316 articles out of 445 BIM articles (i.e. 71 percent) utilize the case study as the research method, this implies that BIM articles' authors prefer case study approaches in their studies. Basbagill et al. (2013) argued that case study research design is the best approach to introduce new concepts to industry practice and since BIM is still relatively new to the construction industry; and this can be adduced as the reasons behind the use of case studies for BIM researches. 83 More so, Davies and Harty (2013) believed case study research method does help to challenge fundamental misconceptions when applying innovative technologies to practice; although, case study approach has no need for internal validation (Hartmann et al., 2012). Furthermore, the analysis of the articles’ corpus shows that “questionnaire surveys and interviews” research approaches with 47 articles (11 percent) and “general discourse and literature reviews” approaches with 38 articles (8 percent) are also quite common among BIM authors. 3.4.4.3 BIM Software and Data Schema The bibliometric analysis of the BIM articles in this section assesses the frequency of usage and mentions of different BIM software and data standard as analyzed from the BIM publications’ corpus. An analysis of the articles reveals that there are eleven (11) BIM data schemas in use in the construction industry; and in fact, the industry foundation class (IFC) and the green building eXtensible markup language (gbXML) are very popular among BIM authors with 187 and 30 mentions in the articles’ corpus (see Table 3.9). However, of the two interoperability standards, IFC is mainly employed in the AEC industry (Belsky et al., 2014; Karan & Irizarry, 2015; Tashakkori et al., 2015). More so, the high usage of the IFC schema is because of its continuous improvement so as to advance interoperability among BIM software (Kota et al., 2014), although it is not yet at a satisfactory level (Aram et al., 2013). Meanwhile, the MVD schema is only a subset of the IFC schema (Lee et al., 2013a). Also, the IFCXML schema is another subset of the IFC schema that allows the IFC to be transmutable over the web (Redmond et al., 2012). Furthermore, unlike IFC, the gbXML data schema is specifically developed for energy analysis while the IFC data format is a schema developed by buildingSMART and can be used for data exchange across the building life cycle (Kim et al., 2012). Furthermore, the analysis of BIM software for architectural and structural designs reveals 10 software in use in the AEC sector, of which Autodesk Revit with 132 articles’ mentions is the most used software; previous studies unveiled similar findings (Kim et al., 2015b; Wang et al., 2015b). Survey results by Bynum et al. (2013) revealed that Revit is the dominant BIM authoring tool in the AEC industry with 78 percent of respondents utilizing it. However, there was low usage of BIM 84 energy analysis software such as Ecotect, IES Virtual Environment. Nevertheless, no single BIM application can support all the process functionalities required in the AEC industry. 85 Table 3.4: BIM journal publication lists and Impact factors S/N Journals Total 1 2 Applied Energy Environmental Science and Technology 2 1 Impact factor 5.746 5.393 3 5 5.288 34 4 5 6 Computer-Aided Civil& Engineering Journal of Cleaner Production Waste Management Building and Environment IEEE Transactions on Engineering Management International Journal of Precision Engineering and Manufacturing Journal of Bridge Engineering 1 1 6 4.959 3.829 3.394 35 36 37 Sustainable Cities and Society Scientia Iranica Structural Design of Tall and Special Buildings 3 1 1 1.044 1.025 0.898 7 8 9 10 Resource Conservation and Recycling Energy Policy Expert Systems with Applications Energy and Buildings 2 1 1 10 3.28 3.045 2.981 2.973 38 39 40 41 2 2 1 1 0.893 0.871 0.801 0.776 11 1 2.534 42 1 0.770 12 IEEE Transactions on Intelligent Transportations System Automation in Construction Journal of Performance of Constructed Facilities HVAC&R Research Journal of Transportation Engineering Journal of Industrial and Management Optimization Transportation Research Record 177 2.442 43 11 0.750 13 14 Construction and Building Materials Applied Mathematical Modelling 2 1 2.421 2.291 44 45 Journal of Asian Architecture and Building Engineering Journal of Environmental Protection and Ecology Studies in Informatics and Control 1 1 0.734 0.723 15 16 17 18 Building Research and Information Safety Science Carbon Management Advanced Engineering Informatics 2 4 1 40 2.196 2.157 2.092 2.000 46 47 48 49 Mathematical Problems in Engineering KSCE Journal of Civil Engineering Canadian Journal of Civil Engineering Advances in Structural Engineering 1 10 6 1 0.689 0.600 0.586 0.577 19 20 Journal of Computing in Civil Engineering Journal of Management in Engineering 36 14 1.855 1.840 50 51 2 11 0.559 0.538 21 22 Journal of Building Performance Simulation Research in Engineering Design 1 1 1.807 1.786 52 53 1 1 0.526 0.429 23 Measurement 1 1.742 54 86 International Journal of Engineering Education Journal of Professional Issues in Engineering Education and Practice PCI Journal Proceedings of the Institution of Civil EngineersStructures and Buildings International Journal of Civil Engineering 1 0.372 Infrastructure S/N Journals Total 32 33 1 1 Impact factor 1.103 1.075 1 1.069 S/N Journals 24 Advances in Engineering Software 25 26 27 28 29 Journal of Civil Engineering and Management Engineering with Computers Building Simulation Sustainability International Journal of Computer Integrated Manufacturing Structure and Infrastructure Engineering Journal of Construction Engineering and Management 30 31 Total S/N Journals 3 Impact factor 1.673 Total 55 2 14 1 3 3 1 1.530 1.460 1.409 1.343 1.319 56 57 58 59 60 Proceedings of the Institution of Civil EngineersCivil Engineering Journal of the Chinese Institute of Engineers Research Journal of Chemistry and Environment Informes de la Construccion Ashrae Journal Gradevinar Impact factor 0.348 4 1 3 5 3 0.246 0.240 0.227 0.223 0.158 1 26 1.202 1.152 61 62 Civil Engineering Intelligent Computing Architecture 1 1 0.153 N/A in Engineering and 363 82 Total 445 Note: Year (Number of Publications) - 2006 (2), 2007 (3), 2008 (7), 2009 (6), 2010 (17), 2011 (32), 2012 (28), 2013 (63), 2014 (96), 2015 (114), 2016 (77). N/A – Not available Impact Factor as at year 2016. 87 Table 3.5: BIM Research Categories Code BIM- Research Categories Number of articles RC1 IT-enabled simulations and Visualisation 40 RC2 Building design and Energy conformance 65 RC3 BIM Software & Data schema 54 RC4 BIM model development 52 RC5 BIM learning, adoption & practice 70 RC6 Construction and Project Management 78 RC7 Safety/ Risk Management 31 RC8 Facility management 37 RC9 Sustainability 17 RC10 Literature review 1 88 Table 3.6: Theme loading for the BIM research areas Code (research Research areas/themes category) RC1 RC1.1- Emergencies sensing localization; RC1.2- Real-time and 3D visualizations RC1.3- Fire safety simulations RC1.4- Point-cloud data extraction RC1.4- Automated or semi-automated generation of data RC1.5- Virtual support systems RC1.6- Synchronous online collaboration Number of themes 6 RC2 RC2.1- Building energy regulations RC2.2- Code checking & compliance RC2.3- Building envelope cost and energy performance RC2.4- Energy management and analysis RC2.5- Structural analysis and design RC2.6 Building rating systems and assessments RC2.7- Tracking of design changes and errors RC2.8- Daylighting profiling RC2.9- Design validation and coordination 9 RC3 RC3.1- Interoperability RC3.2- Usefulness, benefits, and limitations of BIM applications RC3.3- Augmented reality system RC3.4- Industry Foundation Class (IFC); RC3.5- BIM & Semantic web interoperability RC3.6- Semiotic User interface analysis RC3.7- Cost Estimating BIM tools RC3.8- Software coupling RC3.9- BIM data exchanges RC3.10- Mobile BIM RC3.11- Data schemas (GbXML, City GML, MVD, etc.) 11 RC4 RC4.1- Quantity take-off RC4.2- As-built BIM creation RC4.3- Domain vocabulary & Ontology development RC4.4Model validation process RC4.5- Cloud-BIM RC4.6- BIM collaborative system RC4.7- Semantic web technology RC4.8- Physical BIM library RC4.9- Scan-to-BIM techniques RC4.10- Open query language for BIM RC4.11- nD developments RC4.12- Mapping of BIM & domain knowledge RC4.13- Discrete Event Simulation (DES) model RC4.14- Information extraction RC4.15- Topological information extraction model RC4.16- Java-based BIM 16 RC5 RC5.1- BIM usage and adoption RC5.2- BIM curriculum development RC5.3- Construction stakeholders’ BIM adoption strategies RC5.4- BIM teaching and support; RC5.5- Drivers of BIM adoption RC5.6- Cost-benefit analysis of BIM implementations RC5.7- BIM adoption barriers RC5.8- BIM standardization & Intellectual property rights RC5.9- BIM competency assessment RC5.10- BIM ethics & professionalism RC5.11- BIM practice paradigms & governance approach. 11 89 Code (research category) RC6 Research areas/themes Number of themes RC6.1- Construction planning and monitoring RC6.2- Schedule optimization RC6.3- BIM governance platform RC6.4- Material management & quality assessment RC6.5- Waste management RC6.6- Supply chain management RC6.7- Lean construction management & BIM RC6.8Construction sequencing & logistics optimization RC6.9- Real-time progress management RC6.10- Labour productivity assessment RC6.11- Automated, rule-based constructability checking RC6.12- 3D compliance checking RC6.13- Project cost control RC6.14-Project delivery and asset management RC6.15Construction knowledge management RC6.16- BIM-based procurement framework RC6.17- RCE-procurement RC6.18- Construction collaborative networks. 18 RC7 RC7.1- Automated safety planning and management RC7.2- Risk knowledge management RC7.3- Walkability & hazardous area identification RC7.4- Site risk identification RC7.5- Search & Rescue algorithm RC7.6- Fire safety management RC7.7- Workspace safety and requirements RC7.8 Automatic safety checking of construction models. 8 RC8 RC8.1- Automated access control RC8.2- Defect management system RC8.3- Earthquake damage assessment RC8.4- Performance-based maintenance RC8.5- FM data extraction & conversion RC8.6- Indoor emergency response facilitation RC8.7- Virtual retrofit RC8.8- Tracking the built status of MEP works R8.9-Failure root-cause detection RC8.10- Security analysis RC8.11- Localization of RFID-equipped assets RC8.12- Handover model RC8.13- Building maintenance RC8.14- Energy retrofitting RC8.15- Facilities lifecycle information on RFID tags RC8.16Heat flow modeling RC8.17- Image-based verification of as-built documentation. 17 RC9 RC9.1- Sustainability performance of building RC9.2- Environmental impact evaluation RC9.3- Integrating BIM & LEED RC9.4- Sustainable energy usage RC9.5- BIM-based decision support for master planning RC9.6- Sustainability Appraisal (steel design) RC9.7- Indoor environmental quality (IEQ) RC9.8- Sustainable design and construction RC9.9- 3D analysis of lifecycle assessment RC9.10- Sustainable Material selection. 10 RC10 RC10.1- Pattern and trend in BIM research. 1 90 Table 3.7: Funding structure 2016 Funded articles 40 2015 67 29 2014 46 2013 Year Percent (%) 17.3 Research categories RC1 Funded articles Percent (%) 21 9.1 RC2 40 17.3 19.9 RC3 35 15.2 32 13.9 RC4 32 13.9 2012 10 4.3 RC5 26 11.3 2011 20 8.7 RC6 39 16.9 2010 8 3.5 RC7 15 6.5 2009 5 2.2 RC8 16 6.9 2008 1 0.4 RC9 7 3 2007 2 0.9 RC10 0 0 2006 0 0 Total 231 100 231 100 Table 3.8: Articles' project sector analysis Project Sector Aspects covered Building and Single family houses; Residential floors; Parking garage; Storeys Housing buildings; High-rise structures; Building component and elements, Sports Number of articles 347 center, Educational buildings Transportation Bridges; Highways; Tunnel construction; Subways; Airports; Subway 17 stations; Railways; Railway tracks Environment Safe walking environment; Safety; Traffic noise control; Earthquake 24 damage assessment; Waste effectiveness; Hazard identification & prevention; Safety planning; Work-Space Planning Education BIM curriculum development; BIM implementation; BIM in Quantity 22 Surveying practice; BIM teaching; Developing Students’ Collaborative Skills; Course developments Energy Natural gas plant construction; Gas pipeline; Renewable energy system 5 Applies to all project sectors (Procurement, Estimating, Construction 28 infrastructure General projects & Built environment) Urban Post-earthquake operations (such as search and rescue, and damage regeneration assessment) 91 2 Table 3.9: BIM Software and data schema BIM Software category Software (frequency of mentions or usage) Data Schema Number Industry Foundation Classes [IFC]- 187; GbXML [green building XML]- 30 MVD [model view definitions]- 22; IFCXML- 22; AecXML5; City GML- 4; Omni class- 2; NBDM [TrySys]- 1; bcXML1; LandXML- 1; EcoXML- 1 Architectural and Structural Autodesk Revit- 132; Graphic Soft ArchiCAD- 67; tools AutoCAD- 28; Bentley Architecture systems- 26; Vico Constructor- 13; Digital Projects- 8; Nemetschek AllPlan8; Nemetschek Vectorworks- 4 Google SketchUp- 3; Autodesk Inventor- 1 Building Energy Analysis & Energy Plus- 24; Ecotect- 17; IES VE- 10; eQuest- 10; Simulation Tools DOE-2.2- 7; Radiance- 6 Estimating Tools Quantity take-off- 3; On-screen take-off- 2; Innovaya- 2; BuildSoft Estimating software- 1; CATO CAD- 1; Estimator- 1 Structural Tools* Tekla structures- 30; SAP- 2; MIDAS- 2; STAAD Pro V8i1; ADAPT (Structural Concrete Software)- 1 Construction Management Navisworks- 27; MS Excel- 13; MS Project- 11; DProfilerTools 3 Sustainability Analysis Green Building Studio- 13; IES Virtual Environment- 10; Project Vasari- 1 Model Viewer Solibri Model Viewer- 32; Bentley Micro Station- 13 11 Geographic Information ESRI’s ArcGIS- 4 System Ontology Development Tools Protégé- 2 1 3.5 10 6 6 5 4 3 2 1 Discussions on the BIM Research Categories This section provides a semantic link between the defined BIM research categories and research areas/themes that were established in section 3.4.2 with a view to providing a more qualitative analysis of the articles in alignment with the categories descriptions. Therefore, we conducted a critical appraisal of selected articles based on their relevance to the established BIM research categories. To conserve space, we reviewed four research categories out of the ten defined BIM research categories, which are presented in the following sub-sections. Moreover, a total of 175 articles were examined in the section. 92 3.5.1 Construction and Project Management (RC6) The research category “construction and project management (RC6”) is the most trending BIM sub-field (with 78 articles) among the ten identified research categories, and it also has the highest number of research areas with 18 themes as seen in Tables 3.5 and 3.6. Trending topics in this field include RC6.1- “construction planning and monitoring” (Faghihi et al., 2016; Kim et al., 2016a, 2014); RC6.2“schedule optimization” (Altaf et al., 2014; Chen et al., 2013; Faghihi et al., 2014; Gelisen & Griffis, 2014; Kang et al., 2016; Kim et al., 2013b; Liu et al., 2015a; Moon et al., 2014; Tserng et al., 2014; Wang et al., 2014a); RC6.3- “BIM governance platform” (Dossick & Neff, 2010; Farr et al., 2014; Knight, 2008); RC6.4- “Material management & quality assessment” (Chen & Luo, 2014; Francom & El Asmar, 2015; Kim et al., 2015d), and RC6.5- “Waste management” (Akinade et al., 2015; Liu et al., 2015c; Porwal & Hewage, 2012; Won et al., 2016). sMore so, other research areas focus on RC6.6- “supply chain management” (Babic et al. 2010; Irizarry et al. 2013; Lu et al. 2016); RC6.7- “lean construction management & BIM” (Dave et al., 2016; Sacks et al., 2010a, 2010b); RC6.8“construction sequencing & logistics optimization” (Han et al. 2015; Kumar & Cheng 2015; Marzouk & Abubakr 2016); RC6.9- “real-time progress management” (Kim et al., 2013a; Matthews et al., 2015); RC6.10- “labour productivity assessment” (Lee et al. 2014; Poirier et al. 2015); and RC6.11- “automated rule-based constructability checking” (Hu et al., 2016b; Nahangi & Haas, 2016). Furthermore, other research themes under RC6 are RC6.12- “3D compliance checking” (Jiang & Leicht 2015; Nahangi & Haas 2014); RC6.13- “project cost control” (Turkan et al., 2013; Wang et al., 2014b); RC6.14-“project delivery and asset management” (Choi et al., 2014; Tsai et al., 2014c); RC6.15- “construction knowledge management” (Lin 2014; Peterson et al. 2011; Wang & Leite 2016); RC6.16- “BIM-based procurement framework” (Goedert & Meadati 2008); RC6.17“RCE-procurement” (Grilo & Jardim-goncalves 2011; Grilo & Jardim-Goncalves 2013), and RC6.18- “construction collaborative networks” (Abedi et al. 2016; Grilo et al. 2013; Neath et al. 2014). Furthermore, Fan et al. (2014) carried out eight (8) case studies research to assess the effect of BIM on construction projects on aspects such as request for information 93 (RFI), reworks, schedule compliance and change orders. The findings of the case studies reveal a marked reduction in the RFI between 50 and 90 percent, fewer or no modification(s), and compromises in the project with improved quality. More so, there was more accurate schedule compliance with shorter duration and altogether less reworks and significantly fewer change orders. Meanwhile, to facilitate real-time visualization of BIM models; Johansson et al. (2015) developed a prototype BIM viewer. Tan (2017) use the genetic algorithm (GA) technique to optimize for the visualization of lift planning in offshore rigs. Other studies include BIM for infrastructural projects (Bradley et al. 2016), the development of a prototype BIM-GIS architecture to facility management practices (Kang & Hong, 2015); the use of geospatial and semantic technologies for pre-construction operations (Karan & Irizarry 2015), BIM for quality assurance (Kim et al., 2016c); BIM for work sequencing (Kim et al., 2016b); project progress and productivity improvement (Matthews et al. 2015; Nath et al. 2015), and BIM for as-built documentation (Park & Cai 2017). In summary, there has been a significant increase in the number of publications classified under the labeled theme “construction and project management” between 2013 and 2016, in fact, 65 out of the 78 BIM articles under this research area were published during this period. More so, of the 18 BIM research areas under RC6, we have four themes with more than 6 articles, these include RC6.2- “schedule optimization” with 11 articles, RC6.9- “real-time progress management”, RC6.18“construction collaborative networks” and RC6.15- “construction knowledge management” with 9, 6 and 6 BIM articles respectively. The review and analysis of the BIM articles reveals the benefits of BIM in construction and project management as (1) to facilitate collaboration and coordination among construction stakeholders; (2) to optimize the construction schedule, (3) to track the progress of work on site, and (4) to serve as hub or central house for the management of construction information and processes. 3.5.2 BIM learning, adoption & practice (RC5) The next identified BIM research category is “BIM learning, adoption & practice” with 70 BIM articles and under this category are 11 research area/themes as shown in Tables 3.5 and 3.6. Without the framework to aid and strengthen the implementation 94 of innovative technology, approaches or techniques in a diverse and competitive industry like the construction industry such technology or approach may not seek the light of the day. The previous statement enforces the importance of BIM paper in this research category. More so some of these studies and research themes covered include: RC5.1- “BIM usage and adoption” (Eadie et al., 2013; Fortner, 2008; Gilkinson et al., 2015; Kim et al., 2016d); RC5.2- “BIM curriculum development” (Pikas et al., 2013; Sacks & Pikas, 2013; Sampaio, 2015; Solnosky et al., 2014; Wu & Issa, 2014a); RC5.3“construction stakeholders’ BIM adoption strategies” (Ahn et al., 2016; Jung & Lee, 2016; Salleh & Fung, 2014; Xu et al., 2014); RC5.4- “BIM teaching and support” (Gnaur et al. 2015; Kim 2012; Kovacic et al. 2015; Sacks & Barak 2010); RC5.5“drivers of BIM adoption” (Mom et al., 2014a); RC5.6- “cost-benefit analysis of BIM implementations” (Giel & Issa 2013; Lu et al. 2014). Other research themes in this research category are RC5.7- “BIM adoption barriers” (Chien et al. 2014; Watson 2011); RC5.8- “BIM standardization & intellectual property rights” (Fan 2014; Howard & Björk 2008; Kraatz & Hampson 2013); RC5.9“BIM competency assessment” (Giel & Issa 2016; Succar et al. 2013; Wong et al. 2015); RC5.10- “BIM ethics & professionalism” (Jaradat et al. 2013; Love et al. 2015; Succar 2009); and RC5.11- “BIM practice paradigms & governance approach” (Alreshidi et al. 2016; Becerik-Gerber & Kensek 2010; Hanna et al. 2013; Hanna et al. 2014; Rezgui et al. 2013; Samuelson & Björk 2016; Taylor & Bernstein 2009; Won & Lee 2016). A study by Lee and Yu (2016) compared the acceptance level of BIM in South Korea and the United States of which the data were collected using interviews and questionnaire surveys. Their findings revealed a higher adoption and user satisfaction rate in the US than those in South Korea. More so, an ethnography research conducted by Mahalingam et al. (2015) on two metro railway stations projects in India, exemplified the effect of BIM in the decision-making process leading to precise planning and reduced contract duration. The rapid growth in BIM adoption in the Swedish construction industry was elucidated in a study by Samuelson and Björk (2014) while Zhang et al. (2016) developed a framework to facilitate the integration of BIM and sustainability studies into the curriculum development for civil engineering students. Also, some set of principal areas or 95 factors to consider in the adoption and implementation of BIM in an organization has been developed (Won et al. 2013). Conclusively, there have been a steady increase in the number of BIM articles published under the category- “BIM learning, adoption & practice” between 2013 and 2016; however, prior to this period, less than four BIM articles in this research area were disseminated. Meanwhile, BIM research in this category had focused mostly on RC5.1- “BIM usage and adoption” and RC5.11- “BIM practice paradigms & governance approach” (with 13 articles each); we can then surmise that BIM being a novel approach in the built environment has led BIM authors to direct their attention to the core of its adoption and practice and to set up a governance mechanism to facilitate its implementation. Next, are research themes such as RC5.2- “BIM curriculum development” and RC5.4- “BIM teaching and support” with 7 articles each; this analysis reveals the increasing spotlight on the development of BIM module and training for undergraduate university students and professionals who would be the fulcrum in the adoption and implementation of BIM. Therefore, based on the analysis of this category, we deduce the critical drivers of BIM adoption to include: (1) The development of undergraduate BIM curriculum and modules which should incorporate the practical aspects of BIM to train students who are potential ‘recruits’ to the industry. (2) The institution of a training and support programs such as workshops, seminars, and conferences on BIM to aid the skill sets and development of in-house personnel on the use of BIM. (3) Establishment of a working BIM governance mechanism or framework to support its overall implementation and increase the success rate of BIM-enable projects. 3.5.3 Building design and Energy conformance (RC2) The research category of "Building design and Energy conformance (RC2)” is another major area of immense publication and interest among BIM authors and in the construction industry with 65 BIM articles as at the year 2016. The bibliometric analysis of its research areas and themes crystallize out nine (9) main BIM research themes which are: RC2.1- “building energy regulations” (McGuire et al. 2016; Thompson & Bank 2010); RC2.2- “code checking & compliance” (Dimitrov & Golparvar-Fard 2015; Jung et al. 2015; Li et al. 2014); RC2.3- “building envelope, cost and energy performance” (Ahn et al., 2014; Asl et al., 2015; Chardon et al., 96 2016; Gökçe & Gökçe, 2013; Migilinskas et al., 2016); and RC2.4- “energy management and analysis” (Gökce & Gökce 2014; Kim & Anderson 2013; Kim & Yu 2016a; Lee et al. 2016). Meanwhile, other preeminent research themes in this category include: RC2.5“structural analysis and design” (Bosché & Guenet, 2014; Ham & Golparvar-Fard, 2015; Lee et al., 2012b; Marzouk & Abdelaty, 2014b); RC2.6- “building rating systems and assessments” (Basbagill et al. 2013; Oti et al. 2016; Ryu & Park 2016); RC2.7- ”tracking of design changes and errors” (Dong et al., 2014; Lee et al., 2015a; Pilehchian et al., 2015); RC2.8- “daylighting profiling” (Welle et al. 2012), and RC2.9“design validation and coordination” (Gimenez et al., 2016; Kim & Jeon, 2012; Kim & Yu, 2016b; Lee et al., 2012a; Shin & Cho, 2015). Meanwhile, a bibliometric analysis of this category reveals more BIM publications in areas such as RC2.5- “structural analysis and design” with 13 articles and RC2.3- “building envelope, cost, and energy performance” with 12 BIM articles. Other aspects such as RC2.9- “design validation and coordination” and RC2.4“energy management and analysis” with 12 and 10 BIM articles respectively are current research directions in this category. Authors with research interest in “building design and energy conformance (RC2)” tends to center the studies mostly in these four main themes; and it signifies the increasing importance of developing BIM models for projects which are of (1) high structural design and integrity; (2) validated and vetted designs, and (3) a profiled and efficient energy usage and management. Studies such as Ham and Golparvar-Fard (2015) developed a system using the gbXML schema to improve the energy performance of buildings while query systems based on IFC schema have also been advanced (Gao et al. 2015; Kang 2017; Solihin et al. 2017). More so, Kim et al. (2015b) developed a physical BIM library to aid the simulation of building component thermal conditions; while Kim et al. (2015a) observed that most energy analysis for buildings are done when the design has been completed. Hence, they developed an IFC framework to map building materials with energy properties of which the results shows “significant gain in accuracy.” Meanwhile, Gökçe and Gökçe (2014) introduced an efficient integrated energy platform for the residential buildings, while Shiau et al. (2012) utilized Ecotect software to improve the energy usage of old structures; and studies such as Cho et 97 al. (2014) and Knight et al. (2010) discusses the benefits of BIM in HVAC design and placement of reinforcement bars in concrete slabs respectively. Other researches focus include case studies reviews on structural BIM (Robinson 2007); quantitative assessment of carbon-dioxide emission (Jun et al. 2015); strategies for design error management (Al Hattab et al. 2015); creation of BIM models from laser scanner data (Xiong et al. 2013), and the design of track alignment using BIM (Huang et al. 2011). 3.5.4 Sustainability (RC9) Sustainability is one of the increasing and preeminent issues in the construction industry and in other sectors of the global economy, while the concept of sustainable development represents a pyramid shift in the three-wheel drive of construction projects otherwise known as the “project management triangle” which are the time, cost and quality. The adoption and implementation of sustainable practices in construction ensure such projects meets its environmental, social and economic needs, considerations and implementation. BIM publications in this category are on the increase since the year 2011 till date with ten (10) main research themes has identified by the bibliometric analysis of this category articles’ corpus. The predominant research areas in this category include: RC9.1- “sustainability performance of building” (Jrade & Jalaei 2013; Kreiner et al. 2015); RC9.2“environmental impact evaluation” (Lee et al., 2015b); RC9.3- “integrating BIM and LEED” (Azhar et al., 2011; Jalaei & Jrade, 2015; Wu & Issa, 2014b); RC9.4“sustainable energy usage” (Azzi et al., 2015; Liu et al., 2015b); RC9.5- “BIM-based decision support for master planning” (Kim et al., 2015c); RC9.6- “sustainability appraisal” (Oti & Tizani, 2015; Wong & Kuan, 2014); RC9.7- “indoor environmental quality (IEQ)” (Marzouk & Abdelaty 2014a); RC9.8- “sustainable design and construction” (Bynum et al. 2013; Geyer 2012); RC9.9- “3D analysis of lifecycle assessment” (Inyim et al. 2015; Kulahcioglu et al. 2012); and RC9.10- “sustainable material selection” (Bank et al. 2011). Prominent studies on sustainability issues in construction projects include Oti et al. (2016) who utilized the BIM API extension to embed sustainability issues to simulate the assessment of structural steel design; while Oh et al. (2015) in a case study approach, reviewed the enhancement of the design quality of a hospital design using an integrated system. Ilhan and Yaman (2016) advanced an IFC-based sustainability 98 decision support system (“Green building assessment tool (GBAT)”) of which green building data can be certified for BREEAM (Building Research Establishment Environmental Assessment Method). 3.6 Research Limitations The main limitation of this study’s bibliometric analysis is the literature search strategy.That is, the choice of the scientific database (ISI Web of Science) which despite being “the world’s leading citation database, offering a high level of accuracy and detail on a multidisciplinary scale” (Neto et al. 2016), it may only represent a fraction of the whole population. Another drawback might evolve from the exclusion of articles not written in English and some other false positives in the removal of some unrelated papers or the categorization of items within research areas. 3.7 Conclusions The advent of BIM in the construction industry has brought about tremendous improvement in the process and system of coordinating construction projects and enabling collaboration among professionals both in the academia and the industry. The research’s objectives were to investigate and evaluate the extant literature on BIM; and identify the trends, research impacts, research categories, BIM funding structure and other parameters of the research publications’ corpus through a bibliometric analysis of 445 BIM articles; which are of high impact factors from the Web of Science which Neto et al. (2016) regarded as the “largest and most reliable source for academic publications”. The level and depth of the bibliometric analysis is considered as the prime distinction between this study and previous literature reviews on BIM literature; which allows academics, industry practitioners and readers to track the funding structure of BIM research, the research categories and the project sectors for which BIM has had the most impact; and have an overview of how BIM literature has evolved over the years. Moreover, based on the bibliometric analysis of the BIM articles, there was a marked increase in the number of BIM articles from 17 papers in 2010 to almost double value of 32 BIM articles in 2011 and the volume of BIM publications crossed the threshold of a hundred (100) BIM papers in the year 2015 with a total of 114 published BIM articles. 99 Also, an analysis of the articles’ corpus journal list reveals Automation in Construction as the journal with most published work on BIM themes. More so, the tremendous impacts of BIM implementation in the construction industry were felt most in the building and housing project sector with more than 340 BIM articles addressing issues such as building elements and components, etc.. Other project areas such as transportation, environment, and even the educational sector had been positively influenced by the adoption of BIM with several articles developing frameworks, models, systems and providing innovation solutions to improve the identified sectors using BIM. More so, after the bibliometric analysis of the BIM articles, we further endeavor to classify them into 10 research categories, and the core research categories based on published BIM works are (1) Construction and Project Management. (2) BIM learning, adoption, and practice. (3) BIM design and energy conformance. (4) BIM software and data schema; and (5) BIM model development. The five research categories had more than 50 BIM articles each and are considered the salient BIM research areas. Nevertheless, research areas such as facility management and sustainability can be classified as the latest trends in BIM research with increased output in publications in those two categories since the year 2014. Furthermore, an analysis of funding structure of BIM articles reveals that more than 50 percent of the 445 analyzed articles received some funding to undertake the research; while further funding inquiry-based research category analysis and research origin analysis reveals thus: (1) there has been a significant increase in funded articles since the year 2013 till date, (2) BIM articles relating to research categories such as “building design and energy conformance”, “construction and project management”, “BIM software & data schema” and “BIM model development” have received more funding (with at least 30 funded articles); (3) the Republic of Korea and the United States with 68 and 45 funded BIM publications are the countries whose BIM researchers have received a sizeable number of research grants to undertake BIM-related research. However, funding analysis by regions shows Asia with 112 articles, Europe with 57 articles and North America with 55 BIM articles are the regions with the most funded BIM publications. Moreover, a further analysis of the research categories reveals 107 research themes. The analysis unfolds trending research themes and direction, both in the 100 academia and in the industry and these include: “BIM usage and adoption (RC5.1),” “BIM practice paradigms & governance approach (RC5.11)”; “structural analysis and design (RC6.9),” “building envelope, cost and energy performance (RC2.3),” “design validation and coordination (RC2.9)”; and “energy management and analysis (RC2.1)” with more than 10 BIM articles each. The result is a pointer to the fact that more studies are being conducted to investigate BIM adoption and implementation in several countries and domains, and in countries where BIM has reached an acceptance level of adoption and compliance, such as the US, research tends to focus on developing and introducing BIM governance mechanisms. Other findings evaluate the salient research methodology used in previous studies and the available BIM software and data schema while the discussion centered on the established BIM research categories and themes. However, for potential future research, researchers can select one or more research category or theme and undertake a review using the same or different research approach. The study’s bibliometric analysis identified some essential gaps and opportunities for future research in BIM field. Research areas such as (1) BIMSustainability issues integration, (2) Using BIM for environmental and socioeconomic evaluations, (3) integrating BIM and Augmented reality during the construction phase, (4) Ontology and semantic web, (5) Mapping of BIM & domain knowledge and (5) information extraction are currently not receiving adequate considerations from researchers in the academia and those in the industry. It is noteworthy that these research areas when given due attention have the benefits of enhancing the growth of the construction sector and boost its productivity level. Conclusively, the study would assist BIM researchers and other academics to recognize the pattern and structure of BIM research and field and help them to pinpoint areas of research interest for their future research works. 3.8 Chapter Summary Building Information Modelling (BIM) processes have continued to gain relevance in the Architectural, Engineering, and Construction (AEC) industry with more resources directed toward it. This study conducts a bibliometric analysis of 445 BIM articles to investigate and understand the pattern of BIM research which include defining BIM research categories, evaluating the project sectors that are influenced by BIM, and 101 tracking the funding structure of BIM research. A network map that displays a visualization of the structure of BIM literature by research origin, funding structure and geographical scope was designed. None of the previous reviews of literature analyzed the BIM articles’ corpus to such level and depth. The findings revealed research categories such as construction and project management and BIM learning, adoption & practice as the core research areas in BIM and highlighted trending research themes in BIM research. Authors based in Asia and Europe received more research grants than their counterparts in other regions; likewise, twothird of the articles were authored by academics in the United States, Korea, and the United Kingdom. This chapter provides its readers with relevant research areas that require considerations, and the discussion of selected research areas provide an extensive understanding of salient BIM fields. The next three chapters (Chapters 4, 5, and 6) will discuss the findings of the identification and assessment of the inherent benefits, barriers and drivers associated with integrating BIM and sustainability principles in building projects towards fulfilling Objective #1 of this study. The following chapter examines the benefits of the implementation of BIM and sustainability practices – otherwise known as smart sustainable practices in this study – in building projects and in the built environment. 102 CHAPTER 4: AN EMPIRICAL SURVEY OF THE PERCEIVED BENEFITS OF EXECUTING BIM AND SUSTAINABILITY PRACTICES IN THE BUILT ENVIRONMENT4 4.1 Chapter Overview The previous chapter presented a comprehensive reviews and bibliometric analysis of BIM literature and illustrated using network maps the structure of BIM trends. This chapter identifies and examines the benefits of the implementation of BIM and sustainability practices – otherwise known as smart sustainable practices in this study – in building projects as well as in the built environment. Two datasets were collected namely via Delphi surveys (14 experts participating) and international survey involving 220 respondents. However, to conserve space, only the result of the international survey findings will be presented in this chapter. A link to the published findings of the Delphi survey conducted in this research is provided in section 4.4 of this chapter. 4.2 Introduction The input of technological innovations and salience to sustainability issues in the construction industry has been argued as the best approach for the built environment to achieve its goal of a sustainable smart city and buildings. Aasa et al. (2016) noted that sustainable development is achievable through the implementation of green innovations which involve implementing sustainable solutions using adaptable technologies. An excellent example of a versatile technology is the Building Information Modelling (BIM) system which is described by Olatunji et al. (2017b) as a set of applications and process capable of generating and managing project information throughout the project development phases with numerous benefits to the project stakeholders. This chapter is largely based upon the following published papers: Olawumi, T.O., & Chan, D.W.M. (2019a). An empirical survey of the perceived benefits of executing BIM and sustainability practices in the built environment. Construction Innovation: Information, Process, Management, 19(3), 321–342. https://doi.org/10.1108/CI-08-2018-0065 Olawumi, T.O., & Chan, D.W.M. (2018d). Identifying and prioritizing the benefits of integrating BIM and sustainability practices in construction projects: A Delphi survey of international experts. Sustainable Cities and Society, 40, 16–27. https://doi.org/10.1016/j.scs.2018.03.033 4 103 Malleson (2012) noted that BIM adoption had improved significantly in the United Kingdom (UK) as well in North America (Bernstein et al., 2012); and a sizeable number of contracting and client’s organizations have switched to 3D CAD from 2D CAD. Leveraging on this significant improvement in BIM adoption and implementation in the industry, project stakeholders can enhance the adoption of sustainability practices by developing new tools and plugins where existing ones might be limited in its functionality. Abanda and Byers (2016), and Bynum et al. (2013) reported that building facilities account for 32 percent of global energy consumption and one-fifth of the associated greenhouse gases (GHS). Hence, Gourlis and Kovacic (2017) reported that emerging technologies such as BIM offers promises in the optimization of energy needs as well as identification of the potentials in synergizing building envelope and services to reduce the carbon footprints of buildings. A practical example is a real-life case study building project in which BIM was used to model the energy performance (one of several sustainability parameters) which yielded a significant energy cost savings across the building lifecycle. Also, Tsai et al. (2014b) test-run a customized BIM tool for a design firm. Also, Oti et al. (2016) demonstrated the use of Application Programming Interface (API) in BIM tools to appraise the ability of BIM to embed sustainability ontologies as a new approach to assess some ‘quantitative’ parameters of sustainability. BIM without doubt promising and innovative tool capable of changing the landscapes of construction processes and activities even though, according to Oti et al. (2016), and NIBS (2007), BIM is still a maturing technology. Oti et al. (2016) noted that the existence of some proprietary functions in BIM and the flexibility to add plugins had extended its capacity to address issues such as sustainability as well as for end-user customization. More so, Tah and Abanda (2011) also explored the use of semantic web technology and ontologies to represent sustainability knowledge, although, semantic web technologies are still new, it offers a good prospect to assess sustainability parameters and ease the decision-making process. Moreover, current application of BIM to sustainability practices include (i) lifecycle cost assessment (LCA) (Lundin & Morrison, 2002; Soust-Verdaguer et al., 2017); (ii) sustainable design (Bynum et al., 2013). (iii) Sustainable material selection (Govindan et al., 2015); (iv) waste management (Akinade et al., 2015); (v) 104 daylighting simulation and analysis (Kota et al., 2014); (vi) energy consumption and performance (Abanda & Byers, 2016; Kuo et al., 2016); and (vii) carbon footprint (Shadram et al., 2016). Habibi (2017) examined the potential of BIM to improve the energy efficiency and indoor environmental quality of building facilities. Clearly, these emphasized the need to explore the benefits of BIM and sustainability practices to provide evidential support and to aid clients along with project teams in their quest to adopt green BIM in their projects. Bring the perspectives together; the current study intends to bridge the gap in the knowledge and practice by identifying and assessing the practical benefits to the construction industry and the built environment when BIM and sustainability practices are implemented in construction projects. The study will consider the benefits from the various viewpoints of construction professionals such as civil engineers, construction managers, architects as well as those from diverse organizational setups such as public and private clients, contractors and project consultants. The study will also attempt to classify the identified green BIM implementation benefits as well as provide strategies and recommendations for construction organizations, project teams, local authorities and other key stakeholders towards enhancing the uptake of green BIM initiative in construction projects. Throughout the literature and in practice, we have seen construction projects which either adopt BIM or sustainability practices with varying project success and results. However, this study addresses the benefits achievable in projects in which the clients or the project team intends to use innovative technology such as BIM to amplify the sustainability practices in construction projects. The findings are expected to apply to any buildings projects whether residential, commercial or industrial buildings; and with a focus to facilitate the support and commitment of clients and key project stakeholders by presenting the key benefits achievable via the use of BIM to enhance sustainable parameters of their projects. More so, the integration of BIM and sustainability practices implies the use of BIM technologies such as BIM software, cloud-BIM, plugins such as those developed by Oti et al. (2016) and the use of semantic web technology (Tah & Abanda, 2011) among others for sustainability assessment and simulation in projects. It is advisable according to Ghaffarianhoseini et al. (2017) to leverage on technology tools such as 105 BIM to reinvent the current design and delivery practices in the industry. Hence, it is conceivable that integrating the two concepts in construction projects will assist the project team to exploit the benefits of adopting innovative technologies as well as achieve objectives such as green buildings and neighborhoods, reduced carbon footprints, etc. 4.2.1 The impact of the implementation of BIM and sustainability practices The capacity of BIM as a platform that allows for multi-disciplinary data to be embedded in a single model (Azhar et al., 2011) provides project teams the opportunities to incorporate sustainability parameters into such building models; this informs the basis for simulating and analyzing the sustainability performance of buildings and for comparison purposes (Ahn et al., 2014; Olawumi et al., 2017). The impact of the adoption of BIM and sustainability practices have been identified in the literature as shown in Table 4.1 (Olawumi & Chan, 2018d). Wu and Issa (2014b) stressed the importance of the implementation of green BIM in a project, and that it not only ensures the project team achieves the intended project goals and outcomes but also ensures the targeted sustainability goals are realized. Kats et al. (2003) noted that although it might cost a project like two percent increase of the initial cost of the construction, such projects stand to make significant savings in the lifecycle costs of the project which can be up to 20 percent of the initial construction cost. Hence, per Azhar et al. (2011), green buildings are economically viable with little or no environmental impact on the locality. McGraw-Hill Construction (2010) stressed that the proper integration of green BIM in construction would enable project teams to successfully steer a construction project which is usually complex and sophisticated in a collaborative manner (Ayegun et al., 2018; Olatunji et al., 2017a; Olawumi & Ayegun, 2016). Also, Azhar et al. (2011) and Bynum et al. (2013) noted that the adoption of green BIM helps to facilitate a better decision-making process among the project stakeholders and aid the sustainability analysis of design models at the early stages of a project; and these combine to promote the project’s sustainability goals. Meanwhile, to assess the level of the implementation of green BIM, countries such as the United States, the United Kingdom, South Korea, Japan, Hong Kong has develop BIM standards and sustainability rating system to rate the performance of 106 buildings (Azhar et al., 2011; Olawumi et al., 2017; Wong & Zhou, 2015). The standards provide the prerequisites to be met by a building or facility before it can be classified as a green building and contains practical and measurable criteria for implementing BIM and sustainability in those regions. Kriegel and Nies (2008) highlighted areas in which BIM can aid the sustainable design and these include daylighting analysis, selection of sustainable materials, selecting a good building orientation in order to reduce energy consumption, water harvesting and energy modeling among others. Saka et al. (2017) highlighted the significance of energy and its consumption to the economic development and growth of a nation. A survey study of 145 design and construction firms by Azhar (2010) revealed that a good number of the firms achieved time and cost savings when they implemented green BIM in their projects. Meanwhile, Azhar et al. (2011) conducted a case study analysis on a project and discovered that there is no relationship between some sustainability parameters (like those of LEED) and some BIM-sustainability based analyses. However, it demonstrated the potential benefits of the synergy of BIM and sustainability towards the implementation of smart, sustainable cities. Also, Antón and Díaz (2014) while highlighting the necessity for the construction industry to adopt green BIM observed that 40% of total waste and resources are consumed in construction projects globally and that another 40% of energy consumed in the European Union is from the industry. Hence, Welter (2003) argued for construction stakeholders participation in its implementation, while Kummitha and Crutzen (2017) calls for local authorities to enact laws to promote the concept of sustainable smart city initiative which is the ultimate goal of implementing green BIM in the built environment. Olawumi and Chan (2019c) meanwhile, developed a benchmarking model to facilitate BIM implementation in developing countries and produce an assessment template for a comparative evaluation of BIM projects. Pérez-Lombard et al. (2008) indicated that the energy consumption of buildings in recent years had exceeded those of other major sectors like transportation due to the growing demand for building services and human comforts. These various viewpoints from the literature further underlined the relevance of the integration of BIM and sustainability practices in the construction industry. A review of the literature shows increasing research on BIM and sustainability (Olawumi et al., 2017; Olawumi 107 & Chan, 2017; Wong & Zhou, 2015). Goldman et al. (2002), and Frankel and Turner (2008) buttressed the influence of BIM and sustainability in some reviewed LEED certified projects which shows significant energy savings in most of the building projects evaluated. The positive contribution of green BIM to the construction sector according to De Jong et al. (2015) has triggered a welcoming impression on local authorities in some major cities in the world who are now trying to upgrade the public infrastructure to create a better sustainable and attractive environment for its residents as well as enhancing the cities’ overall competitiveness. Given the above reviews from existing studies, the subsequent aspects of the study will examine and assess the perception of the 220 survey participants on the benefits of the implementation of BIM and sustainability practices. Table 4.1: Summary of identified benefits of the implementation of BIM and sustainability practices Code BN1 BN2 BN3 BN4 BN5 BN6 Benefits References Enhance overall project quality, productivity, and efficiency Schedule compliance in the delivery of construction projects Predictive analysis of performance (energy analysis, code analysis) Improve the operations and maintenance (facility management) of project infrastructure Reduction in cost of construction works and improvement in project’s cost performance Improve financial and investment opportunities BN7 BN8 Reduction in the cost of as-built drawings Facilitate sharing, exchange, and management of project information and data BN9 Facilitates resource planning and allocation BN10 Reduction in site-based conflicts BN11 Ease the process to obtain building plan approvals and construction permits BN12 Support collaboration and ease procurement relationships BN13 Reduced claims or litigation risks BN14 Increase firms’ capability to comply with prevailing statutory regulations BN15 Better design products and facilitate multi-design alternatives BN16 Facilitate building layout flexibility and retrofitting BN17 Real-time sustainable design and analysis early in the design phase BN18 Facilitate, support and improve project-related decisionmaking BN19 Improved organization brand image and competitive advantage 108 Azhar (2011) Azhar (2011); Philipp (2013) Lee et al. (2015b) Azhar (2011) Bynum et al. (2013) Ku and Taiebat (2011); Lee et al. (2012a) Boktor et al. (2014) Olatunji et al. (2017b); Wong et al. (2014) Akintoye et al. (2012) Hanna et al. (2013) Antón and Díaz (2014) Aibinu and Venkatesh (2014); Olatunji et al. (2016b) Bolgani (2013) Aibinu and Venkatesh (2014); Antón and Díaz (2014) Lee et al. (2012a) Webster and Costello (2005) Alsayyar and Jrade (2015) Sacks et al. (2010a) Antón and Díaz (2014) Code Benefits References BN20 Enhance business performance and technical competence of professional practice BN21 Enhance innovation capabilities and encourage the use of new construction methods BN22 Prevent and reduce materials wastage through reuse & recycling and ensure materials efficiency BN23 Reduce safety risks and enhance project safety & health performance BN24 Control of lifecycle costs and environmental data BN25 Facilitate the implementation of green building principles and practices BN26 Ease the integration of sustainability strategies with business planning BN27 Minimize carbon risk and improve energy efficiency BN28 Improve resource management and reduce environmental impact across the value chain BN29 Facilitate the selection of sustainable materials, components, and systems for projects BN30 Higher capacity for accommodating the three pillars of sustainability (social, economic & environmental sustainability) BN31 Enhance the accuracy of as-built drawings BN32 Facilitate integration with domain knowledge areas such as project management, safety, and sustainability BN33 Allow the checking of architectural design of buildings from the sustainability point of view BN34 Facilitate accurate geometrical representations of a building in an integrated data environment BN35 Ability to simulate building performances and energy usage BN36 Encourage the implementation of clean technologies that require less energy consumption Source: Olawumi and Chan (2018b) 4.3 Deutsch (2011) Deutsch (2011) Akinade et al. (2017) Vacharapoom and Sdhabhon (2010) Ku and Taiebat (2011) Wu and Issa (2015) Autodesk (2010a) Wu and Issa (2015) Ajayi et al. (2016) Jalaei and Jrade (2014) Antón and Díaz (2014) Akintoye et al. (2012) Kam et al. (2012) Abolghasemzadeh (2013) Azhar (2011) Aksamija (2012) Bonini and Görner (2011) Benefits of BIM and Sustainability Practices Implementation in Construction Projects Previous studies have demonstrated the endless benefits (see Table 4.1) obtainable when either BIM or sustainable practices are implemented in construction projects. There has also been an increase in cross-field research in BIM and sustainability in recent years (Olawumi et al., 2017; Olawumi & Chan, 2017). Adamus (2013) reviewed some BIM-based sustainability analysis tools and highlighted the benefits that can be gained when full interoperability is achievable between BIM design and analysis tools. Accordingly, the author argued for the development of the current data formats such as gbXML and IFC towards facilitating sustainable development. 109 However, the previous study only highlighted few benefits which are solely related to BIM adoption. Some benefits of adopting BIM in construction projects were also identified by Mom et al. (2014a) and Azhar (2011). One of the key benefits identified by the literature is the use of BIM to identify potential issues relating to the building design, construction, and operation. Also, Olawumi et al. (2017) reported that BIM could be used to advance sustainability practices in construction projects such as the management and profiling of energy usage in buildings. Akadiri et al. (2013) regard BIM as a veritable tool for the selection of sustainable materials for construction projects. The use of BIM software and associated simulation tools to enhance the sustainability parameters of buildings such as to reduce its carbon footprints, improve building energy performances and green neighborhoods is noteworthy. Akinade et al. (2015) developed a BIM-based algorithm to measure the practicability of measuring the deconstructability of building designs to minimize waste and facilitate efficient materials use. GhaffarianHoseini et al. (2017) revealed that BIM has helped project stakeholders to achieve the Australian Green Star rating and improve the design strategy. Also, Khaddaj and Srour (2016) observed that BIM could be utilized to simulate building maintenance and retrofitting; hence when linked with sustainable measures using associated plugins or APIs, it could help advance the implementation of sustainability practices to the facility management stage. Moreover, the aim of implementing these sustainable measures in a construction project is to achieve sustainable development as well as the construction of green buildings which can mitigate against negative of constructed structures on the environment as well as on human lives (Maleki & Zain, 2011). Other positive effects of achieving green buildings or sustainable smart cities are the added benefits on human health, occupant productivity, organizational marketability (Ali & Al Nsairat, 2009) and green neighborhoods. These previous studies have focused mostly on the environmental aspect of sustainable development. Also, according to Ahmad and Thaheem (2017) majority of BIM software available to simulate sustainability parameters focused on the environmental aspect; hence, it is difficult to assess the benefits of using BIM technologies for the three pillars of sustainability. 110 Practical examples of the benefits of BIM implementation in construction projects was illustrated by Abanda et al. (2017) who identified several parameters such as cost, time, quality, productivity, and process, etc. as areas in which the adoption of BIM can profit the construction project. The study also listed some BIM software that is available in the market. Gourlis and Kovacic (2017) enumerated that utilizing BIM to simulate and model the energy needs of industrial buildings can minimize the high energy consumption of such building typologies. Also, the ability of BIM tools to embed other knowledge databases can be advantageous in evaluating some qualitative measures such as some social sustainability parameters. The development of data schemas such as the industry foundation class (IFC) and gbXML allows for data transfer from BIM design tools to simulation tools (Olawumi et al., 2017), although the challenge of interoperability is still prevalent in the industry (Jeong et al., 2016). Huang et al. (2012) underlined the potential of BIM for the management of industrial parks in Taiwan throughout its lifecycle. In the management of these parks, BIM was augmented with other associated tools for GIS, visualization, navigation solutions; which allows real-time monitoring, feedback, and communication. Wang et al. (2013) also utilized BIM to optimize the workflow processes. There are endless possibilities in integrating to different domain areas such safety, scheduling, cost management, procurement, project management as well as sustainability. According to Gourlis and Kovacic (2017), the potential of BIM in sustainability in areas such as building performance is an increasingly exciting research area in the literature. However, the study is advocating a more adept application of BIM to more aspects of sustainability to garner maximum benefits. Meanwhile, some difficulties are still being faced in the industry to advance BIM application in sustainability practices such as interoperability (Kovacic et al., 2013), procedural uncertainties (Gourlis & Kovacic, 2017; Morgan et al., 1992). However, the construction industry will stand to gain more possibilities by deploying BIM infrastructures to amplify sustainability practices in their projects as highlighted in the literature discussed in this section. 111 4.4 Research methodology The study employed a quantitative research design to explore and assessed the benefits derivable by the construction industry when BIM initiatives and sustainability practices are implemented in construction projects. Primary data for the study were collected through empirical questionnaire surveys and secondary data via a review of relevant literature from journal papers, books, and web pages. As perceived by Olatunji et al. (2017a), the instrument and approach to data collection have a significant effect on the achievement of the study’s aims and objectives. The target respondents for the study’s survey are construction professionals and stakeholders with a good knowledge of BIM and sustainability. The survey forms were prepared and sent in three formats to the survey participants, and these include: (1) fill-in PDF survey forms; (2) hand-delivered questionnaires; and (3) online survey forms. Most of the respondents were sent personalized emails with links to the online survey form and an attached fill-in PDF survey form. The questionnaire form and its items were pretested with a few related experts before distribution, and a total of 220 survey responses were received from respondents across 21 countries, and most of the responses were through the online survey form (161 responses), and the rest from hand-delivered questionnaires (45), and fourteen responses were gleaned by means of the fill-in PDF survey forms. The questionnaire form (see Appendix B) solicited necessary information on each respondent as well as their perceptions on the degree of importance of the listed factor items on the benefits on BIM and sustainability practices on a 5-point Likert scale (1=strongly disagree, 3=neutral and 5=strongly agree). The data collected were analyzed using statistical tools as explained in later sections of this paper. Meanwhile, prior to the empirical international questionnaire survey, a Delphi survey was conducted involving 14 experts across eight countries towards reaching a consensus on the cross-field research topic. See Olawumi and Chan (2018d) for the published findings of the Delphi survey exercise. 4.4.1 Statistical tools and reliability test analysis Descriptive and inferential statistical tools were employed to analyze the survey data, and these include: (1) Cronbach’s alpha reliability test, α; (2) Mean ranking (M) and standard deviation (SD); (3) ANOVA, post-hoc Tukey tests & correlation analysis; 112 and (4) Factor analysis and groupings. More so, before subjecting the data to further statistical analysis as recommended by Field (2009), a reliability test was conducted to assess the questionnaire items and its associated scale whether it measures the right construct (Field, 2009; Olawumi et al., 2018). The Cronbach’s alpha value for the study was 0.968 which is higher than the minimum threshold of 0.70 (Olawumi & Chan, 2018c, 2019d) which implies a good internal consistency and that the questionnaire scale measures the right construct which makes the data suitable for further analysis (Olawumi & Chan, 2018b). For this study, in a case where two or more items have the same mean value, the values of their SD are used to rank them. Items with smaller SD’s values are ranked higher (Olatunji et al., 2017a; Olawumi & Chan, 2018d), and in cases, the items have the same mean and SD values, the items will be allocated the same rank. 4.4.2 Demographics of the survey participants Survey participants from 21 countries participated in the study’s questionnaire survey. Figure 4.1 shows the demographics of the working experience of survey participants in the construction industry with varied lengths. The two-hundred and twenty (220) participants have practical knowledge and understanding of BIM and sustainability concepts with 43.2 percent of the respondents having at least a high level of awareness of BIM process and a higher percentage (52.8%) with at least a high degree of awareness of the sustainability process. Also, 36.8 percent and 35.9 percent of the survey participants rated their level of awareness of BIM and sustainability respectively as an average. These findings lend credibility to the data and opinions collected from the survey respondents. 113 Figure 4.1: Profile of working experience of survey participants Meanwhile, the analysis of the respondents’ demographics based on their organization type reveals that majority of them are from the academics and the public-sector clients (which included local authorities and policymakers, etc.) with 39.5% and 25% respectively of the 220 survey respondents. Also, the analysis reveals the respondents from the main contractors (15.9%), project consultants (11.4%) and private clients (5.5%) as one of the significant groups of respondents for the study. Moreover, an analysis of the survey participants revealed the quantity surveyors (25%), researchers (13.2%), architects (12,7%), project managers (12.3%), and civil engineers (10.9%) as the most represented professional groups among the survey participants. Furthermore, 56.8 percent of the survey participants suggested the planning stage at the best stage to implement BIM and sustainable practices in a project. Also, 37.3 percent of the respondents argued for the design stage, while 5.5 percent of the respondents considered the construction stage as the best time. 114 4.3 Analysis and discussion of survey results This section presents the data collected via the empirical questionnaire surveys and discusses the findings of the statistical tools utilized in the study. 4.3.1 Descriptive statistical tests The mean values for the 36 identified benefits range from M= 4.30 (SD= 0.784) for “BN8 - facilitate sharing, exchange, and management of project information and data” to M=3.51 (SD= 1.027) “BN11 - ease the process to obtain building plan approvals and construction permits” at a variance of 0.79 (see Table 4.2). More so, the study adopted the benchmark score of 4 out of 5 on a 5-point Likert scale (Lu et al., 2008; Olatunji et al., 2017a; Olawumi et al., 2018) to identified some factors have highly significant factors. Using this metric, the analysis revealed the top-five key benefits of implementing BIM and sustainability practices in construction projects. These include: “BN8 - facilitate sharing, exchange, and management of project information and data” (M= 4.30, SD= 0.784), “BN1 - enhance overall project quality, productivity, and efficiency” (M= 4.29, SD= 0.700), “BN17 - real-time sustainable design and analysis early in the design phase” (M= 4.20, SD= 0.733), “BN15 - better design products and facilitate multi-design alternatives”, (M= 4.18, SD= 0.796) and “BN22 - prevent and reduce materials wastage through reuse & recycling and ensure materials efficiency” (M= 4.18, SD= 0.828”. The study’s findings and based on the perceptive of the survey participants, it is revealed that the implementation of these two concepts will have a vital effect on not only the final product (buildings and infrastructure) by massively improving it both in design and its efficiency; it can also enhance the process whereby construction products are delivered in the built environment. As advocated by Ayegun et al. (2018), project quality and effective communication is a vital ingredient to measure project success. More so, adopting BIM and sustainability in a project will reduce construction material wastages as well as facilitate the simulation analysis of design alternatives and building performance. Also, there is an agreement by the respondents from various organization setups on some of the key factors such as “BN1 - enhance overall project quality, productivity, and efficiency” which is ranked among the top-five factors by most of the respondents’ groups except the private clients which rated the factor as the eighthranked benefits. The findings is highly expressive and is consistent with the literature 115 (Gu & London, 2010; Olatunji et al., 2017b; Olawumi et al., 2017) which argued that the innovative concepts such as BIM and sustainability has the capacity to improve the stakeholders’ productivity and enhances the chances of achieving the project’s goals. Also, among the professionals such as the architects, project managers, quantity surveyors, and civil engineers; there is a consensus on the five key benefits. Moreover, on factor “BN17 - real-time sustainable design and analysis early in the design phase”, the majority of the respondents’ groups agreed on the factor as a significant benefit derivable when BIM and sustainability practices are implemented in construction projects. However, the project consultants averagely ranked the factor. The differing viewpoint from the project consultants group is consistent with the fact that it might be difficult to conduct a proper sustainable design analysis in the early phase of project design due to: (i) incomplete design and specifications at the early stages of building designs; and (ii) issues with and lack of collaboration and coordination among the project consultants (i.e. architects, structural engineers, building services engineers, etc.) involved in project designs. Also, the findings revealed that implementing these concepts (BIM and sustainability) as little effects on the process of securing building plan approvals and construction permits as well as the reduction of project risks or litigations. Adopting these concepts currently has little effects as captioned above because except in cities like Hong Kong, most governments are yet to enforce and incorporate as an incentive, the implementation of BIM and sustainability practices by clients in their construction projects. 4.3.2 Inferential statistical tests Parametric statistical tools such as ANOVA was employed to investigate the differences in the perception of the various respondents’ groups such as the organization setups (project consultants, public clients, main contractors, etc.) and the professional disciplines (civil engineers, architects, quantity surveyors, etc.). ANOVA is a parametric statistical measure of variance based on the mean of scores (Olatunji et al., 2017a; Tsai et al., 2014a), and for factors that are significant (p<0.05) a further test, which is a post-hoc Tukey test was conducted (Mom et al., 2014a; Olatunji et al., 2017a). 116 4.3.2.1 Statistical tests based on organizational setups The ANOVA carried out on the survey data revealed some significant divergencies (at significance <5%) in the perceptions of the different groups of the respondents’ organization setups on twelve factors. These include “BN1- enhance overall project quality, productivity, and efficiency” [F(5,214) = 2.538, p = 0.030]; “BN3- predictive analysis of performance (energy analysis, code analysis)” [F(5,214) = 3.945, p = 0.002]; “BN4- improve the operations and maintenance (facility management) of project infrastructure” [F(5,214) = 2.312, p = 0.045]; “BN7- Reduction in the cost of as-built drawings” [F(5,214) = 2.373, p = 0.040]; “BN13- reduced claims or litigation risks” [F(5,214) = 2.386, p = 0.039] among others (see Table 4.3). Furthermore, a post-hoc Tukey test was conducted on the twelve significant benefits, of which nine factors were found to be more important (p<0.05). These include “BN1- enhance overall project quality, productivity, and efficiency” with a moderate significance (p=0.014) of which the respondents from the academics (M= 4.48, SD= 0.626) perceived the factor to be of higher importance than their public client counterparts (M= 4.09, SD= 0.752). The high rating given to the factor by the academics could be likely be based on their previous reviews or experience of the impact of these concepts in the construction industry. Also, academics’ perception could be based on happenings in other regions beyond their local context as argued by Olawumi and Chan (2018a), unlike their public-sector counterparts whose perception might be based solely on the impact of these concepts in their locality. Moreover, for “BN3- predictive analysis of performance (energy analysis, code analysis)”, there is a high significance (p= 0.003) between the public-sector clients (M= 3.93, SD= 0.716) and the academics (M= 4.39, SD= 0.653) with the academics rating the factor to be of higher importance the respondents from the public-sector clients. Likewise, at a moderate significance of p= 0.024, the survey participants from the academics perceived the factor to be of higher merit than the project consultants (M= 3.88, SD= 0.881). Evidently, these viewpoints of the respondents’ groups emphasize the role of BIM software to aid the implementation and provide further support to the previous submissions in the literature (Jalaei & Jrade, 2015; Kivits & Furneaux, 2013; Olawumi & Chan, 2018a) that the implementation of BIM and sustainability practices in construction projects will facilitate smart and sustainable urbanization. The interlink between the academics and the industry is getting 117 stronger (Olawumi et al., 2017), and since most research institutes constitute mostly the testbeds for most industrial innovation; it is believed that might have affected the higher rating by the academics for this factor (BN3). See Table 4.3 for the other results of the post-hoc Tukey test analysis. 4.3.2.2 Statistical tests based on professional disciplines The ANOVA statistical analysis conducted on the data collected from the respondents’ groups based on their professional disciplines yield some significant differences (at significance < 5%) in nine-factor such as “BN6- improve financial and investment opportunities” [F(10,209) = 2.519, p = 0.022]; “BN11- ease the process to obtain building plan approvals and construction permits” [F(10,209) = 3.131, p = 0.001]; “BN12- support collaboration and ease procurement relationships” [F(10,209) = 2.068, p = 0.028]; “BN25- facilitate the implementation of green building principles and practices” [F(10,209) = 2.011, p = 0.034]; “BN27- minimize carbon risk and improve energy efficiency” [F(10,209) = 2.150, p = 0.022]. A further analysis using post-hoc Tukey test reveals very high divergencies among the professional groups in three factors only. These include “BN11- ease the process to obtain building plan approvals and construction permits” which shows a very high significant difference (p= 0.000) in the perception of the construction managers (M= 3.18, SD= 0.951) and the academics (M= 4.00, SD= 0.707). Also, between the quantity surveyors (M= 3.71, SD= 0.875) and the construction managers at a very high significance (p= 0.005) for the same factor. The construction managers in both cases ranked the factor below average with their academics’ counterpart ranking it the highest. Although, some countries such as the United Kingdom and Hong Kong has put into place incentives for BIM-compliance firms, however, it is yet to be replicated in the other 21 countries represented in the survey data. When such initiative is introduced in other regions, the factor can be a significant one for the construction industry as projected by the respondents from the academics. Meanwhile, for “BN12- support collaboration and ease procurement relationships” there is a moderate significance (p= 0.035) as the architects (M= 4.11, SD= 0.832) identified the factor to be more important than the construction managers (M= 3.21, SD= 1.182). Although both sets of respondents are significant in the procurement process, the architects utilize the BIM software for building model designs and 118 communicate their designs to other key stakeholders who utilize the designs to simulate various building performance. Hence, architects are more involved in the collaborative activities (especially, at the planning and design stages where these concepts are usually integrated into construction projects), and their perceptions about this factor as one of the benefits of BIM and sustainability practices in the built environment is crucial. Lastly, for “BN28- improve resource management and reduce environmental impact across the value chain” there is a highly significant difference (p= 0.002) between the project managers (M= 4.19, SD= 0.622) and the building services engineers (M= 3.18, SD= 1.015) with the project managers identifying the factor has a more significant benefit of the implementation of BIM and sustainability practices. Also, there is a highly significant divergence (p= 0.005) between the quantity surveyors (M= 4.04, SD= 0.793) and the building services engineers. However, since the project managers and the quantity surveyors are more involved in the management and control of project resources than the building services engineers, their perceptions on this factor will be of more importance to the study. Table 4.2: Benefits of implementing BIM and sustainability practices: inter-group comparisons BN1 Public clients Mea R n k 4.09 2 BN2 3.98 9 4.00 28 3.84 24 3.77 31 4.03 BN3 3.93 13 4.08 23 3.88 22 4.03 15 BN4 3.98 8 4.25 16 4.04 8 3.91 BN5 3.56 31 4.17 19 3.76 30 BN6 3.45 33 4.17 19 3.92 BN7 3.56 32 4.33 8 BN8 4.29 1 4.25 BN9 4.00 6 BN10 4.09 BN11 Benefits Private clients Project consultants Main contractors Mean Rk Mean Rk Mean Rk 4.33 8 4.20 1 4.20 3 Academic s Mea Rk n 4.48 1 Overall Mean SD Rk F Sig. 4.29 .700 2 2.538 .030 26 3.97 .784 26 1.264 .280 4.39 4 4.13 .744 9 3.945 .002 24 4.34 7 4.14 .801 8 2.312 .045 3.86 27 3.89 32 3.80 .886 31 1.435 .213 19 3.86 28 3.72 30 3.72 .872 33 2.222 .053 3.96 16 3.97 21 3.89 31 3.83 .976 30 2.373 .040 16 4.04 7 4.09 10 4.48 2 4.30 .784 1 2.081 .069 4.50 3 3.92 17 3.86 29 4.20 13 4.08 .781 14 2.028 .076 3 3.92 31 4.12 3 3.74 32 4.17 19 4.06 .912 16 1.215 .303 3.27 35 3.50 36 3.28 30 3.49 36 3.76 34 3.51 1.027 36 1.938 .089 BN12 3.62 30 4.00 28 3.64 33 3.69 33 3.99 30 3.80 .915 32 1.598 .162 BN13 3.22 36 3.67 35 3.64 34 3.63 34 3.74 35 3.57 .911 35 2.386 .039 BN14 3.44 34 3.92 31 3.44 35 3.54 35 3.85 33 3.65 .876 34 2.243 .051 BN15 3.89 18 4.33 8 4.08 5 4.17 5 4.34 8 4.18 .796 4 2.939 .014 BN16 3.91 14 4.25 15 3.96 13 4.17 5 4.18 15 4.09 .765 13 1.224 .299 BN17 4.02 5 4.42 5 3.92 19 4.14 8 4.41 3 4.20 .733 3 3.400 .006 BN18 4.05 4 4.33 12 3.92 17 4.03 14 4.31 10 4.15 .709 6 2.086 .068 BN19 3.91 15 3.75 34 3.96 14 4.06 13 4.02 28 3.98 .849 24 .410 .842 119 BN20 Public clients Mea R n k 3.95 11 BN21 3.85 20 4.08 24 3.96 14 4.26 1 4.20 14 4.08 .801 15 1.727 .130 BN22 4.00 6 4.08 26 4.08 6 4.23 2 4.34 9 4.18 .828 5 1.835 .107 BN23 3.85 21 4.00 30 3.84 24 3.89 26 4.08 25 3.95 .854 27 .691 .631 BN24 3.85 22 4.17 19 4.00 9 4.11 9 4.16 20 4.04 .799 18 2.116 .065 BN25 3.85 23 4.25 14 4.00 9 3.91 23 4.21 12 4.04 .793 17 2.042 .074 BN26 3.84 24 4.42 5 3.80 26 4.00 17 4.18 17 4.04 .804 19 2.350 .042 BN27 3.69 28 4.00 27 3.88 22 3.89 25 4.10 23 3.92 .821 28 1.925 .091 BN28 3.89 16 4.42 5 3.80 26 3.83 30 4.14 21 4.00 .822 21 1.941 .089 BN29 3.73 27 4.17 19 3.72 31 4.06 12 4.18 18 3.99 .849 23 2.715 .021 BN30 3.64 29 4.17 18 3.76 29 3.94 22 4.03 27 3.90 .875 29 1.927 .091 BN31 3.96 10 4.67 1 4.12 4 4.20 4 4.18 16 4.13 .863 11 2.144 .061 BN32 3.89 19 4.33 12 3.68 32 4.00 16 4.11 22 4.00 .816 20 1.738 .127 BN33 3.89 16 4.58 2 3.96 12 3.97 19 4.29 11 4.11 .775 12 3.288 .007 BN34 3.95 12 4.33 8 3.92 19 3.97 20 4.37 6 4.13 .834 10 3.185 .009 BN35 3.84 25 4.50 4 4.20 2 4.00 18 4.38 5 4.15 .835 7 3.949 .002 BN36 3.76 26 4.08 25 3.80 28 4.17 7 4.08 24 3.98 .860 25 1.555 .174 Benefits Private clients Project consultants Main contractors Mean Rk Mean Rk Mean Rk 3.83 33 4.00 9 4.09 10 Academic s Mea Rk n 4.02 28 Overall Mean SD Rk F 4.00 .825 22 .278 .925 Note: Rk- Rank Table 4.3: Post-hoc Tukey test results for the respondents' groups Factors Organizational setups (sig.) Factors BN28 Professional disciplines (sig.) BN13 Public clients vs Academics* (0.012) Architects* vs. Building Services Engineers BN15 Public clients vs Academics* (0.011) (0.032) BN17 Public clients vs Academics* (0.019) Civil Project consultants vs Academics* Engineers (0.015) (0.031) Academics* vs. Building Services Engineers BN29 Public clients vs Academics* (0.021) (0.001) BN33 Public clients vs Academics* (0.031) Construction Managers* vs. Building Services BN34 Public clients vs Academics* (0.034) Engineers (0.070) BN35 Public clients vs Academics* (0.002) Engineers* vs. Building Services Note: *the respondent’s group considers the factor of higher significance than the other respondent’s group; sig. - significance 4.3.3 Factor Analysis Factor analysis was employed to investigate the pattern of interrelationships among a large set of variables and identifying a smaller number of factors to represent the relationships. The principal component analysis (PCA) of the factor method was 120 Sig. used in this study; the other type is the Promax rotation method (Chan & Hung, 2015). The basic concept of factor analysis (FA) is to use the underlying factors to explain the complex and obscure phenomenon (Xu et al., 2010), interpretation of ‘nonrelated clusters’ (Fang et al., 2004), and define the relationship of interrelated variables (Chan & Choi, 2015). Also, according to Chan and Hung (2015), factors can be rotated in two forms- oblique and orthogonal; for this study, the varimax rotation method, a subset of the orthogonal rotation method was adopted for the PCA. Moreover, for a set of data to be sufficient for factor analysis, it is recommended for the number of variables in relation to the sample size to be in the ratio of 1:5 (Chan & Choi, 2015; Lingard & Rowlinson, 2006). The current study fulfills this requirement, that is, with 36 variables the sample size must not be less than 180, however, this study has 220 responses which is more than the minimum sample size. The KaiserMeyer-Olkin (KMO) tests which evaluate the sampling adequacy shows a KMO value of 0.952 which implies an ‘excellent’ degree of common variance (Field, 2009), and which is above the minimum threshold of 0.50 (Norusis, 1993). A KMO value close to 1 indicates a compact structure of the correlations and indicates that the clusters generated during the factor analysis are distinct and reliable (Chan & Choi, 2015). Meanwhile, the study utilized Bartlett’s test of sphericity (BTS) to examine the suitability of the PCA for factor extraction (Field, 2009), the BTS statistic tests reveal a substantial BTS value (chi-square=5750.610) with a minimal significance value (p=0.000, df=630) which indicates the correlation matrix is not an identity matrix (Chan & Choi, 2015). Given the above, the research data has met the various preconditions required before PCA can be applied to the data for further analysis and discussion. Hence, factor analysis can be carried out with confidence and reliability. The PCA extraction yielded five factors which constitute 64.663% of the total variance explained (see Table 4.4) which is higher than the minimum threshold of 60% (Chan & Choi, 2015; Hair et al., 2010; Malhotra, 1996). Meanwhile, per Proverbs et al. (1997), factors within a cluster with factor loading close to 1.0 have higher significance in the underlying cluster. The 36 factors represented within one of the five cluster factors have a factor loading which is close to 0.50 or higher. Also, 121 according to Chan and Hung (2015), the value of each variable’s factor loading is a reflection of the contribution of the variable to its underlying grouped factor. Table 4.4: Factor structure of the varimax rotation on the benefit's' factors Code Benefits of implementing BIM and sustainability practices Factor 1 – Sustainable building and technical-related benefits BN29 Facilitate the selection of sustainable materials, components, and systems for projects BN25 Facilitate the implementation of green building principles and practices BN35 Ability to simulate building performances and energy usage BN36 Encourage the implementation of clean technologies that require less energy consumption BN26 Ease the integration of sustainability strategies with business planning BN28 Improve resource management and reduce environmental impact across the value chain BN30 Higher capacity for accommodating the three pillars of sustainability (social, economic & environmental sustainability) BN27 Minimize carbon risk and improve energy efficiency BN33 Allow the checking of architectural design of buildings from the sustainability point of view BN34 Facilitate accurate geometrical representations of a building in an integrated data environment BN24 Control of lifecycle costs and environmental data BN3 Predictive analysis of performance (energy analysis, code analysis) BN22 Prevent and reduce materials wastage through reuse & recycling and ensure materials efficiency BN32 Facilitate integration with domain knowledge areas such as project management, safety, and sustainability BN23 Reduce safety risks and enhance project safety & health performance Factor 2 – Efficiency and process-related benefits BN8 Facilitate sharing, exchange, and management of project information and data BN10 Reduction in site-based conflicts BN1 Enhance overall project quality, productivity, and efficiency BN18 Facilitate, support and improve project-related decisionmaking BN2 Schedule compliance in the delivery of construction projects BN9 Facilitates resource planning and allocation BN4 Improve the operations and maintenance (facility management) of project infrastructure BN31 Enhance the accuracy of as-built drawings BN17 Real-time sustainable design and analysis early in the design phase Factor 3 – Performance and knowledge-related benefits BN19 Improved organization brand image and competitive advantage BN20 Enhance business performance and technical competence of professional practice 122 Factor loading Eigenvalue Percentage of variance explained 17.411 48.365 Cumulative percentage of variance explained 48.365 2.171 6.031 54.396 1.399 3.885 58.281 0.758 0.753 0.744 0.736 0.723 0.704 0.700 0.698 0.670 0.637 0.633 0.610 0.511 0.502 0.487 0.711 0.669 0.643 0.594 0.577 0.559 0.477 0.460 0.455 0.710 0.673 Benefits of implementing BIM and sustainability practices Code BN15 Better design products and facilitate multi-design alternatives BN21 Enhance innovation capabilities and encourage the use of new construction methods BN16 Facilitate building layout flexibility and retrofitting Factor 4 – Financial-related benefits BN6 Improve financial and investment opportunities BN7 Reduction in the cost of as-built drawings BN5 Reduction in cost of construction works and improvement in project’s cost performance Factor 5 – Legal and statutory-related benefits BN11 Ease the process to obtain building plan approvals and construction permits BN13 Reduced claims or litigation risks BN14 Increase firms’ capability to comply with prevailing statutory regulations BN12 Support collaboration and ease procurement relationships 4.3.3.1 Factor loading Eigenvalue Percentage of variance explained Cumulative percentage of variance explained 1.169 3.248 61.529 1.128 3.134 64.663 0.637 0.626 0.548 0.728 0.725 0.719 0.756 0.718 0.678 0.552 Discussions of the clustered benefit factors The underlying grouped factors are evaluated in Table 4.5 in descending order of significance based on their factor scale rating (Chong & Zin, 2012; Chan & Hung, 2015) which is based on the variables within each cluster. The factor scale rating is the ratio of the sum of the mean scores of individual variables in a cluster to the number of variables in the underlying grouped factor. More so, per Sato (2005), it is necessary to designate an attributable and collective tag to each cluster factor to ease its description, and according to Chan and Hung (2015), the tags are subjective and is based mostly on the researcher’s intuitions. The study will expatiate on the top-three of the factor clusters to conserve space. Table 4.5: Ranking results of the factor scale rating for the benefit clusters Factor Factor label 2 Efficiency and process-related benefits 3 Performance and knowledge-related benefits 1 Sustainable building and technical-related Factor scale rating 4.1467 Ranking 1 4.066 2 4.0373 3 benefits 4 Financial-related benefits 3.7833 4 5 Legal and statutory-related benefits 3.6325 5 123 4.3.3.1.1 Efficiency and process-related benefits Factor 2 comprises nine benefit-related factors and has the highest factor scale rating (M= 4.1467) of the five clusters. The cluster details how implementing BIM and sustainability practices can reduce site conflicts, enhance the management of project information, improve efficiency and productivity in a project, support the decisionmaking process, and ensure timely delivery of construction projects among others. Abanda et al. (2015) and Hanna et al. (2013) acknowledged that the introduction of BIM software in the construction market had affected the efficiency and quality assurance in a construction project. Olawumi et al. (2017) noted that there are BIM software that can be used for the simulation of sustainability-related issues in a building design such as EnergyPlus, Ecotect, Green Building Studio which can be used for building energy analysis and carbon-emission analysis. Aibinu and Venkatesh (2014) reported that the benefits of the concepts to support and improve the decision-making process would be realistic when key stakeholders collaborate effectively to define the information from BIM software needed to ease the project tasks as well as perform sustainability analysis (Adamus, 2013). Moreover, Boktor et al. (2014) opined that there must be a cost-benefit analysis of the gains of its implementation as well as an assessment of the capacity of the construction workers to grasp the BIM and sustainability processes to ensure a realistic evaluation of the impact of these concepts in a project. Also, Olawumi and Chan (2018d) and Boktor et al. (2014) recommended the development of BIM and sustainability standards in countries which are yet to set up such to be able to have a guideline to measure the impact of the concepts on the built environment. 4.3.3.1.2 Performance and knowledge-related benefits Factor 3 with a factor rating scale of M=4.066 is another significant underlying factor cluster with five key factors with a factor loading of more than 0.5. The cluster is concerned with the impact of BIM and sustainability practices in construction projects and its capacity to improve the competitiveness of a construction firm and its brand image. Also, it is related to its effect on business performance, facilitating better design products, boosting innovation capacities, and aid building layout flexibility and retrofitting. Antón and Díaz (2014) reiterated the benefit of these concepts to include the delivery of better design products which not only improves the well-being and life 124 quality of the users but also improve the energy performance of such facilities. Aibinu and Venkatesh (2014) recognized its impact to enhance the skill sets and technical expertise of key stakeholders in a world in which technological advancement is the order of the day. Moreover, the ability of BIM software to facilitate design and the visualization of what is to be built in a simulated environment allows the detection of any design flaws or operational issues as well as ease the production of multi-design alternatives (Azhar, 2011). Azhar (2011) also reported the economic benefits of implementing BIM in projects with an average performance of 634% on return on investment (ROI) which reveals a high potential for its implementation. Although, there are risks and challenges to the adoption of BIM and sustainability practices in the construction industry (Olawumi et al., 2018), however, its future looks exciting (Azhar, 2011). More so, to achieve the preceding, Antón and Díaz (2014) advocated for the adoption of the concepts at the early stage of a project, improved interoperability among BIM software, and increased research and development in the construction industry. 4.3.3.1.3 Sustainable building and technical-related benefits Factor 1 with a factor rating scale of M= 4.0373, comprises fifteen key factors and eight of these factors has a factor loading of at least 0.70 and are considered significant within the cluster. The cluster is related to the ability of BIM to simulate building performances and energy usage, the ease of selecting sustainable materials and component for a construction project, simplify the implementation of green building principles and the implementation of clean technologies with the minimal use of energy. Also, it includes its capacity to aid the smooth integration of sustainability strategies with business planning, reduce the environmental impact of the project and improving resource management among others. Jalaei and Jrade (2014) attested to the growing concern about the energy performance of buildings and how designers and other consultants have been utilizing BIM tools to make energy-related decisions as well as the selection of the right type of materials and components. Accordingly, they noted that these decisions have a significant impact on the life cycle of a building (Antón & Díaz, 2014). Wu and Issa (2014b) and Olawumi et al. (2017) stressed that despite the emerging success of green BIM in 125 the construction, its full potential is yet to be tapped. Hence, they advocated for the formulation of an effectively BIM execution plan for use in green building projects. Moreover, Akinade et al. (2015) affirmed that one of the essential functions of BIM that has added its acceptability in the built environment is the ability to simulate building energy performance, perform lighting analysis, and the evaluation of design models before actual construction on project site. Also, this allows the project team to identify any potential flaws in design and to select the most cost-effective and sustainable solution among a variety of design alternatives. Antón and Díaz (2014) pointed out that the integration of BIM and sustainability practices to generate synergies would enhance its robustness to tackle the environmental impact of buildings and the simplify the deployment of clean technologies in buildings. Also, Jalaei and Jrade (2014) found that green or sustainable buildings cost far less to operate and are attractive from a commercial perspective. Hence, Wu and Issa (2014b) encouraged key stakeholders to show strong interests in green BIM implementation to facilitate more sustainable projects. Also, it is necessary to enhance the capacities and functionality of existing green BIM software to comply with existing standards and rating systems (Olawumi & Chan, 2018c; Wu & Issa, 2014b). 4.4 Conclusions The primary aim of this study was to review the impact of the implementation of BIM and sustainability practices in the built environment and to assess the potential benefits of its adoption in construction projects, and to construction firms. A review of reported literature formed the bedrock for gathering the thirty-six benefits which formed the questionnaire items sent to the survey participants. A total of 220 respondents from 21 countries participated in the empirical questionnaire survey which constitutes professionals of varied backgrounds and from different organization setups. The diversity of the survey participants was utilized as a basis to compare the ranking patterns and to detect any significant differences in their perceptions of the key factors. Generally, most of the respondents’ groups agreed on “BN1- enhance overall project quality, productivity, and efficiency” as a key benefit of BIM and sustainability practices implementation in construction projects, as the factor most as one of the 126 top-five significant benefit in the groups’ rankings. Also, the factor is consistent with similar assertions in the literature which expressed the capacity of the concepts to enhance the project’s objectives, one of which includes meeting quality and productivity targets. More so, there is also a significant consensus among the groups on factor “BN17- real-time sustainable design and analysis early in the design phase” as one of the crucial impacts of the implementation of BIM and sustainability in projects. Meanwhile, adopting these concepts have little impacts on the ability of a construction firm or project team to secure building plans approval or construction permits. Moreover, the capacity of the concepts to support and enhance the collaborative working environment in the construction industry was highlighted as well as its ability to ease procurement relationship. A factor analysis of the thirty-six benefit factors using PCA method resulted in five underlying clusters with a minimum of three factors and a maximum of fifteen factors; with each underlying grouped factor given an attributable and collective tag which is a representation of its sub-set factors. Moreover, the study conducted further analysis of the ranking patterns of the various respondents’ groups which yielded impressive results of which effective blueprints and recommendation were suggested to increase the uptake of BIM and sustainability practices towards ensuring the construction industry maximizes these benefits. Some of the recommendations highlighted in the study include: (1) Local authorities and government departments should liaise with relevant professional bodies in the built environment towards setting up ‘green-BIM compliance’ incentives to motivate construction firms and clients to implement the concepts in their projects; and (2) Key stakeholders in the construction industry must streamline and improve the structure of their collaboration as well as the need to incorporate (as much as possible) every stakeholder in decision-making at the early stages of project development. More so, for countries, who are yet to develop BIM and sustainability assessment standards; (3) The establishment of such standards is advocated, as this will provide both qualitative and quantitative guidelines to assess the impact of green BIM on the built environment. Also, (4) Enhancing the interoperability and functionality of green BIM software is imperative to the successful implementation of the concepts; (5) Early adoption of green BIM initiative at the planning stage of project development. (6) Increased and targeted research on green BIM; (7) Development of a green BIM 127 execution plan for use in construction projects and to aid project teams. (8) The need for key stakeholders and construction firms to express a keen interest in green BIM adoption in their projects and training of their staff is essential to reap full benefits of the implementation of BIM and sustainability practices in the built environment. It is evident from these significant research findings and collective perspectives that the implementation of BIM and sustainability practices have played an important role and has exerted profound impacts on construction projects and the built environment. It is recommended that future research studies can explore and conduct a quantitative cost-benefit analysis of the gains of green BIM implementation in the construction industry which is expected to provide a sound basis for project comparison and benchmarking. A concerted effort by the various construction stakeholders, local authorities, and policymakers will ensure that these concomitant benefits highlighted in the study are harvested and realized in the built environment. 4.5 Chapter Summary The increasing urbanization of the built environment has bolstered the need to promote green Building Information Modelling (BIM) initiative in new construction projects and the rehabilitation of old premises. The study aims to explore and examine the key benefits of the implementation of BIM and sustainability practices in the built environment. The study gathered the worldwide perceptions of 220 survey participants from 21 countries which were analyzed using descriptive and inferential analytical methods in this chapter. The identified individual benefits of green BIM were further categorized into their underlying clusters using factor analysis. The key benefits are related to enhancing project efficiency and productivity, ensuring realtime sustainable design and multi-design alternatives, facilitating the selection of sustainable materials and components, together with reducing material wastage and project’s environmental impact, among others. The chapter also analyzed and compared the perceptions of the diverse groups of the respondents as well. Effective blueprints and insightful recommendations for enhancing the various stakeholders’ capacities to implement green BIM in their construction projects were put forward to achieve the aim of sustainable smart urbanization. The study identified salient benefits of the adoption of BIM and sustainability practices. The proper integration of these concepts and the execution of the recommended useful strategies by 128 construction stakeholders, policymakers, and local authorities will enable the built environment to reap the gains of its implementation. The following chapter identifies and examines the barriers (impediments) to the implementation of BIM and sustainability practices – otherwise known as smart sustainable practices in this study – in building projects and in the built environment. 129 CHAPTER 5: BARRIERS TO THE INTEGRATION OF BIM AND SUSTAINABILITY PRACTICES IN THE BUILT ENVIRONMENT5 5.1 Chapter Overview The previous chapter identified and analyzed the benefits of BIM and sustainability in building projects and the built environment. This chapter identifies and examines the barriers (impediments) to the implementation of BIM and sustainability practices – otherwise known as smart sustainable practices in this study – in building projects as well as in the built environment. Two datasets were collected namely via Delphi surveys (14 experts participating) and international survey involving 220 respondents. However, to conserve space, only the result of the international survey findings will be presented in this chapter. A link to the published findings of the Delphi survey conducted in this research is provided in section 5.4 of this chapter. 5.2 Introduction The construction industry has been a slow adopter of innovative and smart technology (such as BIM) and implementation of sustainability practices unlike other sectors such as the automobiles. Kim and Yu (2016b) aligned with this viewpoint by revealing that 78% of current users of BIM are yet to utilize this innovative tool for green projects. Apart from the United Kingdom and the United States which have witnessed an improved adoption and implementation of BIM and sustainability, most other countries are still lagging in its execution (Jung & Lee, 2015; Olawumi & Chan, 2018a). Gu and London (2010), while expounding on the readiness and implementation level among countries as regards BIM and sustainability, reported that it varies significantly. Even countries considered to be the early adopters and initiators of these concepts experienced a disproportionate level of knowledge. Meanwhile, according to Kummitha and Crutzen (2017) and Kim and Yu (2016b), sustainable smart approaches have recently been gaining drastic momentum in the This chapter is largely based upon the following published papers: Olawumi, T.O., & Chan, D.W.M. (2020b). Concomitant Impediments to the implementation of Smart Sustainable Practices in the Built Environment. Sustainable Production and Consumption, 21(January), 239-251. https://doi.org/10.1016/j.spc.2019.09.001 Olawumi, T.O., Chan, D.W.M., Wong, J. K. W., & Chan, A. P. C. (2018). Barriers to the Integration of BIM and Sustainability Practices in Construction Projects: A Delphi Survey of International Experts. Journal of Building Engineering, 20, 60–71. https://doi.org/doi.org/10.1016/j.jobe.2018.06.017 5 130 industry. However, due to several inherent challenges in the industry, there are several setbacks which need to be addressed (Olawumi et al., 2018). Sustainable smart practice or approach is a system whereby technological tools and software are employed and integrated to facilitate the implementation of sustainability objectives (environmental, social and economic) in building projects, infrastructures, and urban cities. These practices have improved the efficiency of operations and projects, improved quality of life, among others; and are measured using some established performance indicators. Meanwhile, a desktop review of the extant literature (Ayegun et al., 2018; Kummitha & Crutzen, 2017; Olawumi & Ayegun, 2016) revealed a variety of forces and conflicting expectations due to the multi-stakeholders and layered structure of projects and organizations in the construction industry. Hence, this has made the execution of sustainable smart practices in projects more complex and tasking. More so, the initial cost of acquiring necessary Information and Communication Technology (ICTs) infrastructure which is regarded as the core of smart city initiative (Graham & Marvin, 2001); and central to its successful implementation, of which BIM is a key variety (Olawumi & Chan, 2018d, 2018c) is very high. Although, Neirotti et al. (2014) reported that ICTs alone cannot help achieve the desired improvements in the built environment as regards improving the standard and quality of human lives, and fulfilling the required sustainability potential of buildings. Hence, the need for an evaluation of other concepts that can enhance the sustainability of buildings and cities. This study intends to assess the barriers affecting the adoption and implementation of sustainable smart practices in construction projects. Conversely, the existing literature has discussed some benefits obtainable by the adoption of sustainable smart practices in the built environment. For instance, Bakici et al. (2013) and Olawumi and Chan (2019a) highlighted some benefits of implementing smart, sustainable practices in the construction project which include improving the quality of life of urban dwellers of such cities, enhancing the ability of stakeholders to simulate building energy performance (Olawumi & Chan, 2018d). Moreover, Bradley et al. (2016) stressed the functional capacity of BIM technologies to address issues in other domain areas such as sustainability, project management of which it was not initially designed for its use. 131 5.2.1 BIM and sustainability practices: A review The use of technological tools like BIM for construction processes and sustainability evaluation of projects have gained the immense attention of policymakers, researchers, government agencies and key stakeholders in the construction industry in recent years (Olawumi et al., 2017; Olawumi & Chan, 2017, 2018a). Some current application of 6D BIM (BIM and sustainability) in the construction industry include the application of BIM for sustainable material selection for construction project (Govindan et al., 2015). Also, Akanmu et al. (2015) developed a decision support system (DSS) to enhance the selection and procurement of low-cost and environmental-friendly building materials for different building designs. Also, Aksamija (2012) exemplified the use of BIM analysis tools to simulate building energy performance for a case study project. Other applications of BIM and sustainability include: (1) lifecycle cost assessment (Soust-Verdaguer et al., 2017); (2) simulation of building design performance (Aksamija, 2012); (3) sustainable design (Alsayyar & Jrade, 2015; Autodesk, 2010b); (4) building energy analysis (Ham & Golparvar-Fard, 2015; Kim et al., 2015a); (5) Indoor environmental quality [IEQ] (Habibi, 2017). Also, Olawumi and Chan (2017) developed a geospatial map depicting the distribution of the global sustainability research. Despite these attempts to utilize BIM for sustainability implementation in construction projects as exemplified by the literature, the construction industry is deficient of the necessary collaboration and coordination (Olatunji et al., 2016b, 2017b; Olawumi & Ayegun, 2016) to drive salient issues like sustainability and BIM. Hence, Aksamija (2012) and Olatunji et al. (2016b) argued for a collaborative working environment and an iterative decisionmaking process in the construction industry towards enhancing the capacity of BIM to strengthen the sustainability of the built environment. However, the construction industry is faced with challenges related to the joint implementation of the two concepts in construction projects (Gu & London, 2010). Adamus (2013) pointed out the challenge of developing smart building which is consistent with sustainable development (SD) principles and the need to ensure the achievement of the three pillars of SD. Accordingly, BIM was identified by Adamus (2013) as capable of enabling the construction industry to meet the emerging sustainability requirement and facilitate the sustainability analysis and simulation of 132 building models before construction onsite. Also, Gu and London (2010) pointed out that the readiness of the AEC industry for innovative technology and processes such as BIM varies among countries. Also, even among the early adopters of BIM and initiators of sustainability assessment metric, there is a disproportionate level of knowledge and experience (Olawumi & Chan, 2018d). More so, the level of readiness and implementation is disproportionate among construction organizations and regions (Gu & London, 2010; Redmond et al., 2012) as well as the prevailing resistance to change from traditional working practices (Abubakar et al., 2014) by construction stakeholders have hindered a holistic implementation of BIM and sustainability in construction projects. Given the above, project clients have developed apathy for its adoption in their project (Chan, 2014). Meanwhile, Olawumi et al. (2017) observed that despite growing research and studies in BIM-sustainability issues in construction projects, most projects have focused on one aspect of the three fundamental pillars of sustainable development which is environmental sustainability. Meanwhile, these cross-study BIM- sustainability literature have dealt on energy performance issues in projects instead of a holistic view of what is possible in achieving a sustainable smart city. The current approach to sustainability assessment is still a challenge to the construction sector; this is because the design stage offers the best opportunity to influence sustainability decisions (Ding, 2008; Olawumi & Chan, 2018c, 2018d). There have been some success stories of the use of BIM to enhance sustainability implementation of construction projects in the literature. For instance, the development of a BIM-based Deconstructability Assessment Score (BIM-DAS) by Akinade et al. (2015) who develop a set of metrics that can be utilized in making choices on building designs suitable for deconstruction. However, the model is yet to be integrated as a plugin in BIM software limiting its practical implementation in construction projects. Adamus (2013) reiterated the issue of interoperability as a significant setback affecting the use of BIM to evaluate sustainability parameters of the building model. Cidik et al. (2014) developed an information categorization framework to evaluate design alternatives in BIM environment which not only optimize such designs but also allows for a holistic design sustainability analysis to be undertaken. Jalaei and Jrade (2015) advanced a methodology that integrates BIM with LEED (Leadership in Energy and Environmental Design) building certification 133 system which can assist project teams to make sustainability-related decisions while at the same time ensure such buildings accumulate good points on LEED rating. Key barriers reiterated in the literature hindering the adoption of both concepts (BIM and sustainability) in the construction industry are highlighted in Table 5.1. Previous studies have highlighted the inadequacy of requisite experience, knowledge, and skills from the workforce (Nanajkar & Gao, 2014; Wu & Handziuk, 2013). For these reasons, it is recommended for stakeholders to shore up their knowledge base and learn new skills and as advised by Olawumi et al. (2017), professional bodies and organizations should organized training seminars and workshops for their members and staff and development of university curriculum in BIM and sustainability issues. Without doubts, the backbone of the BIM initiatives and sustainability simulations and practices are technologically enabled software, tools, plugins, and databases. 5.3 Impediments of implementing smart sustainable practices: A desktop review There has been a surge in recent years in the use of variants of BIM in construction process and previous studies such as Wang and Adeli (2014) and Olawumi et al. (2017) stressed the need to integrate smart techniques such as BIM with sustainability to achieve more energy savings, reduce carbon emissions, and promote green neighborhoods. However, as it is always the case when new techniques and concepts are introduced in the construction industry, the implementation of sustainable smart practices are facing some setbacks (Jalaei & Jrade, 2015; Nanajkar & Gao, 2014; Olawumi et al., 2018). One key aspect common to the implementation of smart, sustainable practices is the use of software to model and analysis the building model and associated performance parameters. According to Adamus (2013), there have been issues relating to data exchange between building design software and sustainability analysis software, mostly known as interoperability issues in the construction industry (Olawumi et al., 2017). Technical impediments: Angelidou (2015) observed that technology-based product in the construction industry advanced faster and received more acceptance; although its implementation, according to Olawumi et al. (2018) can be much slower. However, the issues relating to sustainability and providing solutions to the construction industry’s efficiency problems has lagged (Angelidou, 2015); hence, 134 producing an imbalance and hindering the achievement of sustainable development in the built environment (Cugurullo, 2017). As noted by Kummitha and Crutzen (2017), there has been skepticism as smart cities and buildings such as how can is such planned, whose ideas make up the plan and what are the cost and benefits. These issues according to Moser (2015) and Datta (2015a) has heightened apprehensions among communities, its citizens and even among some stakeholders who may be the ‘actual losers’ due to the top-down approach of most innovative smart city initiative which has some negative implications for sustainable urban development (Calzada & Cobo, 2015). Also, according to Alsayyar and Jrade (2015), there is limited sustainability analysis software to support this initiative, and per Akinade et al. (2017), the sustainability parameters of building properties are difficult to access for performance analysis purposes. Legal-related barriers: Kummitha and Crutzen (2017) reported how the government of India enacted some laws to fast-track the use of some specific cities as a platform to support the smart city initiative, however, per Bunnell (2015), the steps suffered some significant setbacks due to protest by marginalized communities who wanted the government to roll-back the scheme. BIM according to Aibinu and Venkatesh (2014) is not made mandatory by most clients for their projects, hence, if any contractor intends to adopt it in such projects, the contractor might likely bear the cost of the implementation. The above brings to the fore, the lack of awareness of this benefits to key stakeholders both in the construction industry and in the local communities (Gu & London, 2010; Hope & Alwan, 2012). Also, in the United Kingdom, it is mandatory for public projects exceeding five million pounds to implement BIM in such projects. Several other factors such as shown in Table 5.1 are some barriers which are evident in the literature and practice as hindering the implementation of smart, sustainable practices in the construction industry (Olawumi et al., 2018). Education and knowledge-related barriers: Welter (2003) argued the need for citizenry participation in the design, building, and management of their buildings and cities in a bottom-top approach to city urbanization. However, currently reverse is the case in the built environment whereby only a few stakeholders are involved in building design and collaboration (Kummitha & Crutzen, 2017); amid the native noncollaborative culture of project stakeholders in the construction industry (Olatunji et 135 al., 2016b, 2017a). More so, Wang and Adeli (2014) argued for the necessity to promote sustainable building design among project stakeholders in order to ensure efficient material use and energy consumption (Lee et al., 2013b; Pinto et al., 2013), reduce carbon emission and lifecycle costs (Hegazy et al., 2012). Stakeholder’s attitude: Abubakar et al. (2014) highlighted the resistance to change of construction organizations and key stakeholders in the built environment as a key impediment to the implementation of innovative concepts such as BIM and sustainability in building projects. Hence, per Gu and London (2010) and Redmond et al. (2012) this has led to the disproportionate level of implementation of sustainable smart practices in construction projects. Abubakar et al. (2014) classified this resistance to change into – societal and habitual resistance. Wu and Handziuk (2013) noted that the resistance to change had impacted negatively on the skills, knowledge, and the experience of project stakeholders as regards sustainable smart practices and its adoption in building projects. Hence, for the built environment to experience a full implementation of these concepts in every construction project; a significant change in stakeholders’ attitude and perception to the uptake of innovative and revolutionary concepts such as BIM and sustainability practices. Organizational and project-related barriers: Antón and Díaz (2014) regard the construction industry as a project-based sector which requires the coordination of various stakeholders from different organizations to collaborate to accomplish the project objectives. More so, per Olawumi et al. (2018) argued that for a successful implementation of BIM and sustainability practices, a considerable measure of physical human efforts and coordination is required. However, as reported by Boktor et al. (2014), the inadequacy of project team coordination, as well as the fragmented nature of the construction industry, have hindered the successful implementation of sustainable smart practices in building projects; especially in labor-intensive projects. These issues highlighted above impedes the delivery of construction projects and the application of innovative technologies and concepts. The study will, in the subsequent sections, attempts to analyze the perception of various stakeholders from twenty-one countries on the barriers to the implementation of sustainable smart practices. 136 Table 5.1: Summary of identified barriers to the implementation of smart sustainable practices Code Barriers Related sources of data BA1 “Varied market readiness across organizations and geographic locations.” BA2 “Industry’s resistance to change from traditional working practices.” BA3 “Lack of client demand and top management commitment” “Lack of support and involvement of the government” “Low level of involvement of BIM users in green projects” “Societal reluctance to change from traditional values or culture” “The lack of awareness and collaboration among project stakeholders” BA4 BA5 BA6 BA7 BA8 “Inadequacy of requisite experience, knowledge, and skills from the workforce” BA9 “Longer time in adapting to new technologies (steep learning curve) ” “Lack of understanding of the processes and workflows required for BIM and sustainability” “Low level of research in the industry and academia” BA10 BA11 BA12 BA13 BA14 BA15 BA16 BA17 BA18 “Inadequate in-depth expertise and know-how to operate sustainability-related analysis software programs” “Shortage of cross-field specialists in BIM and sustainability” “The high cost of BIM software, license, and associated applications” “The high initial investment in staff training costs” “Recurring need for additional and associated resources and high economic expenses” “Lack of initiative and hesitance on future investments” “Fragmented nature of the construction industry” BA22 “Organizational challenges, policy, and project strategy” “Difficulty in assessing environmental parameters of building properties” “Difficulty in accessing sustainability-related data (such as safety, health, and pollution index, etc.)” “The risk of losing intellectual property and rights” BA23 BA24 “Difficulty in allocating and sharing BIM-related risks” “Lack of legal framework and contract uncertainties” BA25 “Increased risk and liability” BA26 “Lack of suitable procurement policy and contractual BA19 BA20 BA21 137 Antón and Díaz (2014); Gu and London (2010); Kivits and Furneaux (2013); Redmond et al. (2012) Abubakar et al. (2014); Gu and London (2010); Kivits and Furneaux (2013); Chan et al. (2019a, 2019b) Aibinu and Venkatesh (2014); Boktor et al. (2014); Rogers et al. (2015) Abubakar et al. (2014); Bin Zakaria et al. (2013) Antón and Díaz (2014); Ma et al. (2018) Aibinu and Venkatesh (2014); Kivits and Furneaux (2013); Redmond et al. (2012) Antón and Díaz (2014); Bin Zakaria et al. (2013); Gu and London (2010); Hope and Alwan (2012) Abubakar et al. (2014); Aibinu and Venkatesh (2014); Chan (2014); Gu and London (2010); Kivits and Furneaux (2013); Nanajkar and Gao (2014) Aibinu and Venkatesh (2014); Nanajkar and Gao (2014) Aibinu and Venkatesh (2014) Aibinu and Venkatesh (2014); Antón and Díaz (2014); Redmond et al. (2012) (Ahn et al., 2014; Antón & Díaz, 2014; Gu & London, 2010) Hope and Alwan (2012) Aibinu and Venkatesh (2014); Kivits and Furneaux (2013); Nanajkar and Gao (2014) Aibinu and Venkatesh (2014); Kivits and Furneaux (2013) Aranda-Mena et al. (2009); Young et al. (2008) Gu and London (2010); Hanna et al. (2013) Antón and Díaz (2014); Gu and London (2010); Kivits and Furneaux (2013); Redmond et al. (2012) Boktor et al. (2014); Dossick and Neff (2010) Abolghasemzadeh (2013); Akinade et al. (2017) Adamus (2013); Antón and Díaz (2014); Olawumi and Chan (2019b, 2019c) Kivits and Furneaux (2013); Redmond et al. (2012) Kivits and Furneaux (2013) Aibinu and Venkatesh (2014); Redmond et al. (2012) Kivits and Furneaux (2013); Olawumi et al. (2018) Aibinu and Venkatesh (2014); Sackey et al. Code BA27 BA28 BA29 BA30 BA31 Barriers Related sources of data agreements” “Non-uniformity of sustainability evaluation criteria and measures” “Lack of a comprehensive framework and implementation plan for sustainability” “Absence or non-uniformity of industry standards for sustainability” “Inaccuracy and uncertainty in sustainability assessments for projects” “Incompatibility issues with different software packages” BA32 “Absence of industry standards for BIM” BA33 “Insufficient level of support from the BIM software developers” “Inadequacy of BIM data schemas to semantically represent sustainability-based knowledge” “Lack of supporting sustainability analysis tools” BA34 BA35 BA36 BA37 BA38 5.4 “Non-implementation of open source principles for software development” “Domination of the market by commercial assessment tools” “User-unfriendliness of BIM analysis software programs” (2015) Abolghasemzadeh (2013); Antón and Díaz (2014) Azhar (2011); Redmond et al. (2012); Saxon (2013) Alsayyar and Jrade (2015); Boktor et al. (2014); Saxon (2013) Ahn et al. (2014); Alsayyar and Jrade (2015); Antón and Díaz (2014) Antón and Díaz (2014); Kivits and Furneaux (2013); Nanajkar and Gao (2014); Rogers et al. (2015) Antón and Díaz (2014); Chan (2014); Redmond et al. (2012); Rogers et al. (2015); Saka et al. (2019a) Redmond et al. (2012) Adamus (2013); Chan et al. (2019b); Olawumi and Chan (2019d) Akinade et al. (2015); Alsayyar and Jrade (2015) Hope and Alwan (2012) Hope and Alwan (2012) Ahn et al. (2014); Aksamija (2012) Research methodology This study identified and assessed the barriers to the implementation of smart, sustainable practices in construction projects. The study adopted a quantitative research methodology via empirical questionnaire surveys to elicit the necessary data for the study. Moreover, the questionnaire items were gathered via the use of secondary data through a systematic review of desktop literature from journal papers, government gazettes, libraries, and web pages. According to Olatunji et al. (2017), the method of data collection is significant in establishing the aim of the study as well as in the composition of the questionnaire survey form. A purposive sampling technique, together with a snowball sampling, was used in targeting relevant respondents for the study. The survey respondents are construction professionals with good knowledge of the concepts of smart, sustainable practices as it relates to the built environment. The respondents were given brief information on what smart sustainability practices is. Three modes were adopted in sending the questionnaire surveys to the respondents: (1) online survey 138 forms; (2) fill-in PDF survey form; and (3) hand-delivered questionnaire. More so, personalized emails were sent to some potential respondents using with the attached fill-in PDF survey form as well as a link to the online survey form. A total of 220 survey responses were received across 21 countries, and the data were analyzed in greater detail in later sections. One hundred sixty-one responses were collected via the online survey form, 14 via the fill-in PDF form and 45 via the hand-delivered method. There was a 100% response rate via the hand-delivery method of the questionnaire distribution. However, for the other two forms of distributions (fill-in PDF form and online surveys), it was difficult to determine the questionnaire return ratio as a snowball sampling technique was used for it. The questionnaire was pretested before distribution. The questionnaire survey (see Appendix B) collected some background information on the respondents as well as asked the respondents to rate the factors on a 5-point Likert scale: 1 = strongly disagree, 3 = neutral / no comment and 5 = strongly agree. The respondents have the option to tick ‘N/A’ if the factor is not applicable as a barrier to the implementation of smart, sustainable practices in construction projects. The respondents were given options to add to the factors listed for assessment. However, none of the 220 respondents added to the 38 factors listed on the survey form. Meanwhile, prior to the empirical international questionnaire survey, a Delphi survey was conducted involving 14 experts across eight countries towards reaching a consensus on the cross-field research topic. See Olawumi et al. (2018) for the published findings of the Delphi survey exercise. 5.4.1 Statistical tools for data analysis Several statistical tools and methods were employed in analyzing the data collected in the course of the study. These include: (1) Cronbach’s alpha (α) reliability test; (2) Mean score ranking and standard deviation (SD); (3) Inferential statistical tests such as ANOVA, post-hoc Tukey tests, correlation analysis; and (4) Factor analysis and groupings. According to Field (2009), a reliability test is required to be undertaken before further analysis on a set of data. Cronbach alpha reliability test was used in this study to assess the questionnaire and its associated scale to ensure its measure the right construct (Field, 2009; Olatunji et al., 2017a). 139 The Cronbach’s alpha is employed to test the internal consistency and reliability of a construct, and the range of its α coefficient ranges from 0 to 1. It implies that the larger the α-value, the better the reliability of the scale or the generated result (Chan et al., 2019b). The arithmetic mean is a measure of central tendency which indicates the average values of a set of figures (equation i) while SD is a quantitative measure of the differences of each value from the mean and it is a measure of variability (see equation ii). A low SD indicates that the values are close to the mean, whereas a high SD implies the data points are spread out over a large range of values. ANOVA (analysis of variance) is an inferential statistical tool used to determine whether any statistically significant differences exist between the means of two or more independent data groups. ANOVA requires typically distributed data points(Olatunji et al., 2017a). The post-hoc Tukey test is regarded as a posteriori test because it is only needed to confirm and reveal where the differences occurred between groups after an ANOVA analysis has identified the statistically significant different groups. Factor analysis is discussed in full details in section 5.5.3. 𝑋̅ = 𝑆𝐷 = √ Where 𝑋̅ = mean score. ∑𝑥 − − − − 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 (𝑖) 𝑛 ∑(𝑥 − 𝑋̅)2 − − − − 𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 (𝑖𝑖) 𝑛−1 ∑ 𝑥 = aggregate score of a set of values. 𝑥 = individual factor value. 𝑛 = number of values (that is, number of respondents in this study). 𝑆𝐷 = Standard deviation. The α-value for this study was 0.951, which is higher than the minimum threshold of 0.70 (Olawumi & Chan, 2018d) and implies good internal consistency and that the data are suitable for further statistical analysis. For the mean ranking, if two or more factors have the same mean value, the SD values are used to rank them; the factor with the lower SD value is ranked higher (Olatunji et al., 2017a; Olawumi & Chan, 2018b). However, if they have the same mean and SD value, they will have the same rank (Olawumi & Chan, 2018c). 140 5.4.2 Respondents' demographics The section reveals vital information about the 220 respondents that participated in the survey (see Figure 5.1). The respondents were from 21 countries working under diverse organizational types with majority of them working in the academia (87, 39.5%), followed by public client participants (55, 25%), main contractors (35, 15.9%), project consultants (25, 11.4%), private clients (12, 5.5%), with the least number of participants coming from property management companies (6, 2.7%). Professional-wise, the findings a slight majority as quantity surveyors (25%), followed by academics (13.2%), architects (12.7%), project managers (12.3%), civil engineers (10.9%), builders and construction managers (8.6%), building services engineers (7.7%), urban planners (2.7%), BIM managers (2.3%), structural engineers (2.3%); and estate valuers and property managers (2.3%). Figure 5.1: Respondents' demographics Meanwhile, the respondents were asked about their level of awareness of the BIM concepts and processes. The findings revealed that a significant percentage of the respondents have at least a high level of awareness (95, 43.2%), while the eight-one of the respondents (36.8%) have an average level of awareness. Meanwhile, based on the respondents’ level of awareness of the sustainability process, most of the respondents (116, 52.8%) have at least a high level of awareness. While about 141 seventy-nine respondents (35.9%) have an average level of awareness of sustainability practices. The survey participants have considerable professional experience in the construction industry with 44.5% of the respondents (98) having at least 11 years working experience in the industry, and the next 27.7% of the respondents (61) have between five to ten years working experience in the construction industry. More so, majority of the respondents (125, 56.8%) argued for the implementation of smart, sustainable practices at the planning stage and another 37.3% of the respondents (82) noted that the design stage of project development is the best stage to implement the concepts while twelve respondents (5.5%) and one respondent preferred the construction and facility management stages respectively. 5.5 Results of statistical analyses This section discusses the results of the data collected via the questionnaire surveys and the findings of the statistical tools employed in the study. 5.5.1 Descriptive statistical tests For the 38 barriers identified, the mean values range from M= 3.32 (SD= 0.984) for “BA25 - increased risk and liability” to M=4.15 (SD= 0.860) for “BA8 - the inadequacy of requisite experience, knowledge, and skills from the workforce” at a variance of 0.83 (See Table 5.3). Moreover, based on similar benchmarks adopted by (Lu et al., 2008; Olatunji et al., 2017a) who utilized the mean value of 4 on a 5-point Likert scale to regard a factor as an important one; a total of five factors can be regarded as significant based on the mean score. These include “BA8 - inadequacy of requisite experience, knowledge, and skills from the workforce” (M=4.15, SD= 0.860), “BA2- industry’s resistance to change from traditional working practices” (M=4.06, SD= 0.868), “BA9- longer time in adapting to new technologies (steep learning curve)” (M=4.02, SD= 0.876), “BA10- lack of understanding of the processes and workflows required for BIM and sustainability” (M=4.00, SD= 0.825), and “BA15- high initial investment on staff training costs” (M=4.00, SD= 0.934). From the research findings, it can be implied that low awareness and knowledge is still a significant hindrance to the implementation of smart, sustainable practices in the built environment along with the sustained archaic industry culture and the costs of investment. 142 The respondents from the public and private clients, project consultant, and the academics rated “BA8 - the inadequacy of requisite experience, knowledge, and skills from the workforce” (M=4.15, SD= 0.860) as the most significant barrier to the implementation of smart, sustainable practices in construction projects. However, the factor was rated by respondents from the main contractors as the second most significant factor who ranked “BA14- the high cost of BIM software, license, and associated applications” as the critical barrier. This findings from the various organizational set up show that the respondents from the main contractors perceived the cost of these concepts as significant because incorporating the cost of these software and its implementation in their work might increase their tender bid sum and put them in an unfavorable position against fellow competitive contractors. However, for the other set of respondents, the findings reveal there is still a lack of knowledge and expertise in both the private and public sectors of the construction industry. The civil engineers, project managers, and quantity surveyors agreed with this finding by ranking factor BA8 as the most significant barrier while the architects perceived “BA9- longer time in adapting to new technologies” (M=4.43, SD= 0.742) as the most critical barrier. The academics regards “BA11 - low level of research in the industry and academia” as the least important barrier, this shows that there is a considerable increase in research publication in BIM (Olawumi et al., 2017) and sustainability (Olawumi & Chan, 2017, 2018a) in the literature. The private client’s respondents considered “BA25 - increased risk and liability” as the least significant factor, while to the public client’s respondents it is “BA22 - the risk of losing intellectual property and rights”. These findings are because the risks and liabilities in most construction projects are passed across to the contractors by both the private and public sectors clients. Hence, these factors have little impacts on their business interests. 5.5.2 Inferential statistical tests In order to further investigate the differences in the perception from the diverse sets of respondents from differing organizational setups (private and public clients, project consultants, main contractors, and academics) and the professionals (architects, researchers, civil engineers, project managers, quantity surveyors, building service engineers, and construction managers). ANOVA was employed to analyze the 38 identified barriers which according to Olatunji et al. (2017a) and Tsai et al. (2014a) is 143 a parametric statistical tool which is based on the mean of scores. More so, Olatunji et al. (2017a) recommended that a post hoc Tukey’s test to be conducted on factors that are significant at p<0.05. 5.5.2.1 Statistical tests based on professional disciplines The ANOVA analysis conducted on the data revealed a significant divergence in the opinions (at significance <5%) among the groups of respondents on six factors which are “BA11 - low level of research in the industry and academia” [F(10,209) = 1.910, p = 0.045]; “BA14 - high cost of BIM software, license, and associated applications” [F(10,209) = 2.079, p = 0.027]; “BA15 - high initial investment on staff training costs” [F(10,209) = 2.532, p = 0.007]; “BA16 - recurring need for additional and associated resources and high economic expenses” [F(10,209) = 3.040, p = 0.001]; “BA36 non-implementation of open source principles for software development” [F(10,209) = 3.002, p = 0.001]; and “BA38 - user-unfriendliness of BIM analysis software programs” [F(10,209) = 3.241, p = 0.001]. A further analysis of the six significant barriers using the post hoc Tukey test revealed a very high significant difference (p = 0.001) on one factor “BA38 - userunfriendliness of BIM analysis software programs”; with the architects (M=4.00, SD=1.054) perceiving it to be more significant than the construction managers (M=2.74, SD=0.991). The finding is consistent with the fact that architects use more software than an average construction manager; hence, if such software is userunfriendly, it might hinder their use of the software. 5.5.2.2 Statistical tests based on organizational setups The ANOVA analysis conducted on the results (at significant <5%) showed some significant differences in the opinions of respondents from diverse organizational setups on ten factors such as “BA4 - lack of support and involvement of the government” [F(5,214) = 3.188, p = 0.008]; “BA5 - low level of involvement of BIM users in green projects” [F(5,214) = 3.599, p = 0.004]; “BA7 - the lack of awareness and collaboration among project stakeholders” [F(5,214) = 2.869, p = 0.016]; “BA10 lack of understanding of the processes and workflows required for BIM and sustainability” [F(5,214) = 2.758, p = 0.019]; “BA19 - organizational challenges, policy, and project strategy” [F(5,214) = 2.673, p = 0.023] among others (see Table 5.3). Moreover, based on the post hoc Tukey test evaluation of the ten significant 144 barriers, eight barriers were found to be more important (p<0.05). These include “BA4 - lack of support and involvement of the government” with a moderate significance (p = 0.024) of which the respondents from the private clients (M= 4.33, SD= 0.651) perceived the barrier to be significant to their adoption of smart, sustainable practices than those from the public-sector clients. The finding is because private clients who are under less control of the governments might not receive funding or support from the government, unlike their public-sector counterparts who receive yearly or quarterly allocations for their operations. More so, for “BA5 - low level of involvement of BIM users in green projects”, there is a high significance (p=0.016) between the public sector (M=3.36, SD=1.025) and private sector (M=4.33, SD=0.492) clients with the private sector identifying the factor to be of higher importance than their public counterparts. Similarly, at a significance of (p=0.023), the respondents from the main contractors (M=4.00, SD=0.804) perceived the factor to be of high importance than the public sector. The analysis is consistent with the findings of Olawumi et al. (2018), which revealed a higher level of involvement of BIM users in green projects in government establishments than in the private sector. See Table 5.2 for the results of the post hoc Tukey tests for the organizational setups. Table 5.2: Post-hoc Tukey test for the organizational setups Factors Organizational Significance Factors setups BA4 Public clients vs 0.024 BA20 Private clients* Organizational Significance setups Public clients vs 0.006 Private clients* 0.003 Public clients vs 0.017 Academics* Main contractors* vs Public clients BA5 Public clients vs Private clients* Public clients vs Main contractors* 0.016 0.023 BA21 Main contractors* vs Public clients Public clients vs Academics* 0.012 0.008 BA7 Public clients Private clients* Public clients Academics* 0.046 0.021 BA30 Main contractors* vs Public clients 0.023 vs vs BA19 Public clients vs 0.021 BA37 Project consultants 0.019 Private clients* vs Main contractors* Note: *organizational setup considers the factor of higher significance than the other organizational setups 145 Table 5.3: Barriers to smart sustainable practices in the built environment: inter-group comparisons Barriers BA1 Public clients Mean Rk 3.98 4 Private clients Mean Rk 4.17 17 Project consultants Mean Rk 3.88 10 Main contractors Mean Rk 3.91 11 Academics Overall 4.01 Rk 4 Mean 3.97 SD 0.805 Rk 7 F 0.452 Sig. 0.811 Mean BA2 3.95 5 4.42 2 3.96 6 3.89 18 4.18 2 4.06 0.868 2 1.326 0.254 BA3 3.62 16 4.00 28 3.92 9 3.97 8 4.01 6 3.90 0.933 9 1.347 0.246 BA4 3.24 35 4.33 10 3.96 7 3.83 23 3.55 24 3.61 1.127 25 3.188 0.008 BA5 3.36 30 4.33 9 3.72 18 4.00 5 3.56 22 3.64 0.963 21 3.599 0.004 BA6 3.56 19 4.08 24 3.76 16 3.86 22 3.49 28 3.63 1.009 23 1.313 0.260 BA7 3.49 25 4.33 13 3.88 11 3.91 12 3.99 7 3.86 0.928 10 2.869 0.016 BA8 4.05 1 4.50 1 4.12 1 4.09 2 4.23 1 4.15 0.860 1 1.110 0.356 BA9 3.98 3 4.42 2 3.92 8 4.00 6 4.01 5 4.02 0.876 3 0.612 0.691 BA10 3.78 9 4.33 10 4.04 2 3.94 9 4.16 3 4.00 0.825 4 2.758 0.019 BA11 3.25 34 3.67 34 3.64 23 3.63 35 3.22 38 3.38 1.051 35 1.554 0.174 BA12 3.85 8 4.25 16 3.80 14 3.97 7 3.71 16 3.84 0.937 12 0.965 0.440 BA13 3.93 6 4.42 2 3.96 5 4.06 3 3.93 9 3.97 0.967 8 0.900 0.482 BA14 3.87 7 4.33 12 4.00 4 4.11 1 3.93 10 3.99 0.981 6 0.768 0.573 BA15 4.00 2 4.42 7 4.00 3 4.00 4 3.91 11 4.00 0.934 5 0.795 0.555 BA16 3.71 11 4.42 7 3.60 29 3.91 10 3.79 14 3.80 0.851 13 1.824 0.109 BA17 3.60 17 3.92 29 3.60 30 3.86 21 3.67 18 3.68 0.911 17 0.595 0.704 BA18 3.62 15 4.42 2 3.68 22 3.91 15 3.84 12 3.80 0.985 14 1.551 0.175 BA19 3.64 14 4.42 2 3.72 19 3.86 20 3.93 8 3.85 0.786 11 2.673 0.023 BA20 3.22 36 4.17 18 3.64 24 3.80 25 3.76 15 3.64 0.862 20 4.416 0.001 BA21 3.29 33 4.00 25 3.60 25 3.91 13 3.80 13 3.68 0.886 16 3.494 0.005 BA22 3.13 38 3.67 35 3.20 36 3.57 37 3.36 33 3.34 1.058 37 1.131 0.345 BA23 3.29 32 3.67 35 3.56 32 3.71 29 3.49 27 3.49 0.986 33 1.042 0.394 BA24 3.47 27 3.75 33 3.72 19 3.80 24 3.56 21 3.60 0.953 26 0.914 0.473 BA25 3.16 37 3.50 38 3.20 35 3.63 34 3.30 35 3.32 0.984 38 1.173 0.323 BA26 3.58 18 4.17 20 3.60 25 3.66 33 3.51 26 3.58 0.992 28 1.685 0.139 BA27 3.51 21 4.25 14 3.68 21 3.74 27 3.66 19 3.67 0.867 18 1.525 0.183 BA28 3.51 22 4.17 18 3.80 12 3.77 26 3.55 23 3.63 0.915 22 1.636 0.152 BA29 3.51 23 4.08 22 3.80 13 3.86 19 3.60 20 3.67 0.908 19 1.405 0.224 BA30 3.29 31 4.08 21 3.76 15 3.89 16 3.54 25 3.59 0.895 27 3.112 0.010 BA31 3.78 9 3.92 30 3.76 17 3.89 17 3.69 17 3.77 0.958 15 0.283 0.922 BA32 3.69 12 4.08 22 3.60 25 3.69 32 3.47 31 3.62 1.098 24 0.862 0.507 BA33 3.51 24 4.25 14 3.48 33 3.57 36 3.29 37 3.47 1.036 34 2.079 0.069 BA34 3.65 13 4.00 25 3.56 31 3.51 38 3.45 32 3.56 0.980 29 0.839 0.523 BA35 3.47 26 4.00 25 3.60 28 3.69 31 3.31 34 3.50 0.939 32 2.125 0.064 BA36 3.53 20 3.75 31 3.32 34 3.71 28 3.48 29 3.53 0.929 30 0.707 0.618 BA37 3.44 28 3.58 37 3.12 37 3.91 13 3.48 29 3.52 0.963 31 2.542 0.029 BA38 3.38 29 3.75 32 3.04 38 3.71 30 3.30 36 3.36 1.039 36 2.603 0.026 Note: Rk- Rank 146 5.5.3 Classification of the key barriers based on factor analysis The study adopted factor analysis to reduce a large number of the barrier factors to a relatively set of variables by investigating the interrelationships between the variables (Hair et al., 2010; Xu et al., 2010). There are two types of factor analysis, principal component analysis (PCA) and Promax rotation method (Chan & Hung, 2015); the PCA was used in this study. According to Chan and Choi (2015), factor analysis (PCA) is a statistical technique used to identify the underlying clustered factors that define the relationships among sets of interrelated variables; and can be used to interpret ‘nonrelated clusters’ of factors (Fang et al., 2004), and explain complex concepts (Xu et al., 2010). Meanwhile, before subjecting the 38 factors to factor analysis, a Pearson correlation analysis was conducted as recommended by Xu et al. (2010), who noted that the statistical method helps to eliminate the existence of any multiplier effects among the variables. Hence, the correlations of these factors were assessed, and 30 factors which are not highly correlated with each other are used in subsequent analysis. The PCA was conducted using varimax rotation method (an orthogonal rotation method) on the thirty non-correlated barriers factors from a sample of 220 responses. The results of the factor analysis are shown in Table 5.4, while the column ‘factor loading’ illustrates the total variance explained by each factor. Lingard and Rowlinson (2006), Chan and Choi (2015) and Chan (2019) recommended that the sample size must be considered sufficient in the ratio of 1:5 (number of variables: sample size) which the current study fulfilled. That is, 30 barrier factors multiplied by five samples required for each factor = at least 150 samples needed to proceed with the factor analysis. Kaiser-Meyer-Olkin (KMO) tests for sampling adequacy and Bartlett’s test of sphericity (BTS) was used to examine the appropriateness of PCA for factor extraction (Field, 2009). The KMO value for the study’s factor analysis is 0.904, which shows an ‘excellent’ degree of common variance (Field, 2009) and above the acceptable threshold of 0.50 (Norusis, 1993). More so, according to Chan and Hung (2015), a KMO value close to 1 indicates that a compact pattern of correlations and that the PCA will generate distinct and reliable clusters. The BTS analyses revealed a substantial test statistic value (chi-square=3413.643) and a small significance value (p=0.000, df=435) which per Chan and Choi (2015) implies that the correlation matrix is not an 147 identity matrix. Therefore, as the various requirements needed to proceed with a factor analysis has been met, the PCA can be applied in this study with for further investigation and discussion; this ensures the research can be conducted with better reliability and confidence. Seven underlying factors were extracted using PCA which represent 65% of the total variance in responses (see Table 5.4) which is above the minimum threshold of 60% (Chan, 2019; Chan & Choi, 2015; Hair et al., 2010; Malhotra, 1996). Table 5.4: Factor structure of the varimax rotation on the key barrier factors Code Barriers to implementing smart sustainability practices Factor Eigenvalue loading Factor 1 – Technical-related barriers BA35 “Lack of supporting sustainability analysis tools” BA36 “Non-implementation of open source principles for software development” BA34 “Inadequacy of BIM data schemas to semantically represent sustainability-based knowledge” BA32 “Absence of industry standards for BIM” BA38 “User-unfriendliness of BIM analysis software programs” BA31 “Incompatibility issues with different software packages” Factor 2 – Attitude-related barriers BA4 “Lack of support and involvement of the government” BA5 “Low level of involvement of BIM users in green projects” BA6 “Societal reluctance to change from traditional values or culture” BA7 “The lack of awareness and collaboration among project stakeholders” BA3 “Lack of client demand and top management commitment” BA11 “Low level of research in the industry and academia” 0.788 0.713 0.710 0.656 0.566 0.502 0.759 2.191 7.302 43.179 1.642 5.473 48.652 0.701 0.627 0.603 0.595 0.404 Factor 3 – Education and knowledge-related barriers BA8 “Inadequacy of requisite experience, 0.735 knowledge, and skills from the workforce” BA9 “Longer time in adapting to new technologies 0.726 (steep learning curve)” BA10 “Lack of understanding of the processes and 0.714 workflows required for BIM and sustainability” BA13 “Shortage of cross-field specialists in BIM 0.668 and sustainability” 148 10.763 Cumulative Percentage percentage of variance of variance explained explained 35.877 35.877 Code Barriers to implementing smart sustainability practices Factor 4 – Legal issues BA25 “Increased risk and liability” BA24 “Lack of legal framework and contract uncertainties” BA26 “Lack of suitable procurement policy and contractual agreements” BA23 “Difficulty in allocating and sharing BIMrelated risks” Cumulative Percentage Factor percentage Eigenvalue of variance loading of variance explained explained 1.446 4.822 53.473 0.782 0.764 0.756 0.633 Factor 5 – Organizational and project-related barriers BA17 “Lack of initiative and hesitance on future 0.653 investments” BA19 “Organizational challenges, policy, and 0.625 project strategy” BA18 “Fragmented nature of the construction 0.614 industry” BA21 “Difficulty in accessing sustainability-related 0.533 data (such as safety, health, and pollution index, etc.)” 1.251 4.170 57.643 Factor 6 – Information and data-related barriers BA30 “Inaccuracy and uncertainty in sustainability assessments for projects” BA29 “Absence or non-uniformity of industry standards for sustainability” BA14 “The high cost of BIM software, license, and associated applications” 1.207 4.024 61.667 1.006 3.353 65.021 0.721 0.676 0.596 Factor 7 – Market-related barriers BA1 “Varied market readiness across organizations and geographic locations” BA15 “The high initial investment in staff training costs” BA2 “Industry’s resistance to change from traditional working practices” 0.648 0.515 0.504 The 30 barrier factors are represented in one of the seven underlying grouped factors, and all the factor loadings of each barrier factors are close to 0.5 or higher as suggested by Chan and Hung (2015) and Chan and Choi (2015). According to Proverbs et al. (1997), the higher the value of the factor loading of an individual factor (which is maximum of 1.0), the higher the significance of the factor to the underlying cluster factor. The factor loading values also reflect how each factor contributes to its underlying grouped factor (Chan & Hung, 2015). The findings reveal a consistent and reliable factor loading and interpretation of the extracted individual factor. 149 5.6 Discussion of survey findings 5.6.1 Discussion of key cluster factors after factor analysis The clustered factors are analyzed in Figure 5.2 in descending order of significance towards interpreting the individual factors linked to them. As suggested by Sato (2005), an identifiable and collective label is attached to each grouped factor of high correlation coefficients; which are themselves a cluster of individual factors. However, per Chan and Hung (2015), these labels are subjective, and each author may come up with different labels. The factor clusters are ranked using their factor scale rating as employed by Chong and Zin (2012) and Chan (2019). The factor scale rating is the ratio of the mean of individual factors within a cluster divided by the number of factors in the cluster (Chan and Hung, 2015; Chan, 2019). Discussion of the key factor clusters will focus on the top-four ranked factor clusters. Similarly, based on the precedent cases in the existing literature (Chan & Choi, 2015; Olawumi & Chan, 2019a; Xu et al., 2010), these studies only discussed top-three of the key cluster factors generated after factor analysis based on their factor scale ratings; and to converse space. Also, one of the purposes of employing the factor scale rating analysis is to highlight more significant cluster factors with relatively higher rating values for further discussion (Chan & Hung, 2015). Figure 5.2: Ranking results of the factor scale rating for the key cluster factors 150 5.6.1.1 Education and knowledge-related barriers Factor 3, consisting of four barrier-related factors, is the highest-rated clustered factor with a factor scale rating of M=4.035. The cluster is related to experience and knowledge of construction organization staff, the steep learning curve, inadequate understanding of smart, sustainable practices processes, and the shortage of crossfield specialists in smart, sustainable practices. Gu and London (2010) observed through their study that little or no attention has been placed on the training of construction professionals to improve their understanding and skills in the adoption of new technologies. More so, Aibinu and Venkatesh (2014) noted that the rapid technological change has reduced the ability of the workforce to adapt and that despite the benefits of these concepts, the current skills shortage in the industry has reduced the potentiality of its positive impact on construction processes. Hence, as advised by Olawumi et al. (2018), professional bodies and construction firms should collaborate to improve the skillsets and capacity of their members and staff in smart, sustainable practices. Gu and London (2010) call for the training of students at an early stage on these concepts for them to appreciate it after their graduation from college. Moreover, the government can support this initiative by training its staff in construction-related departments and parastatals as well as providing financial subsidies to private firms in the training of their workforce. 5.6.1.2 Market-related barriers The next significant clustered factor is factor 7 with three key factors and a factor scale rating of M=4.01. The cluster is concerned with the varied market readiness across construction firms and regions, the high investment cost of training, and the industry resistance to change from traditional working practices. Olawumi et al. (2018) accentuated that despite the benefits of these concepts, little progress has been achieved in implementing BIM and sustainability practices in several countries. Abubakar et al. (2014) pointed out the hesitance of construction stakeholders to new concepts and innovative technologies which has hindered developments in the industry when compared to other sectors of the economy. Kivits and Furneaux (2013) recommended firms to consider its workforce along with the adopted technology to close the gap in the interconnection of the sociotechnical system. Meanwhile, Olawumi et al. (2018) urge construction firms and project stakeholders to 151 be proactive like their counterparts in other sectors in adopting innovative concepts and embrace dynamic and positive developments in the built environment. 5.6.1.3 Organizational and project-related barriers Factor 5 comprises of four barriers with a factor scale rating of M= 3.7525, which are related to construction firms’ hesitance to plan for future investments, challenges related to organizational policies and strategies, fragmented nature of the industry, and the difficulties in accessing sustainability-related data used for the sustainability assessments of buildings. Olawumi et al. (2018) argued that concepts such as smart, sustainable practices despite its revolutionary effects on the built environment still requires the integration of human efforts and strategies which when lacking can amplify its non-implementation in construction projects. Olawumi et al. (2018) revealed the lack of investment in most organizations, which has affected their adoption of smart, sustainable practices. Antón and Díaz (2014) described the construction industry as a project-based sector, and per Boktor et al. (2014) the uncollaborative environment nature of the industry and ineffective organization strategies has hindered the implementation of these concepts. Moreover, Adamus (2013) considered the availability of sustainability-related software and data as pivotal to the decision-making process of project stakeholders and the sustainability assessments of buildings; while, Olawumi et al. (2018) pointed out the need for the government and professional bodies to subsidize the cost of procuring related smart, sustainable practices software to aid its adoption. Overall, the need for the development of sound and effective strategies by construction firms and stakeholders towards the adoption of smart, sustainable practices cannot be overemphasized. 5.6.1.4 Information and data-related barriers Factor 6 is composed of three key factors with a factor scale rating of M= 3.75, and it includes the uncertainty and inaccuracies in sustainability assessments of buildings, the absence or non-uniformity of industry standards for smart, sustainable practices, and the high cost of BIM software and its associated software. Adamus (2013) observed that computer-aided decision tools have the potential to improve the sustainability of the built environment. However, their effectiveness is being hindered by the interoperability between design and sustainability analysis software. Adamus 152 (2013) revealed that some data schemas such as the gbXML lacks contextual information that can aid sustainability assessments of building models. Alsayyar and Jrade (2015) advocated the need for uniform sets of sustainability criteria and a central database to evaluate the sustainability potentials of a building at the design stage. Aibinu and Venkatesh (2014) highlighted the cost of implementation as a significant barrier to the adoption of BIM in Australia, and this includes the high initial cost of the BIM software, yearly licenses or upgrades, and associated applications. Hence, since smart, sustainable practices feed on data as inputs for its effective impacts on the built environment, project stakeholders must collaborate to improve access to relevant data and its exchange. 5.6.2 Practical implications of research findings The current study has revealed salient issues militating against the implementation of BIM and sustainability practices in the built environment, which have a significant impact on the proper delivery of sustainable and smart building projects. As revealed in the research findings, the private sector clients lamented the lack of support and involvement by their respective governments to enable their implementation of sustainable smart practices in building projects. Chan et al. (2019b) reported the initiative of the Hong Kong government to introduce subsidies and credit facilities to private developers and clients to facilitate adopting BIM and sustainability practices in the Hong Kong built environment too much success. Such initiatives are recommended for adoption to governments in other climes to embrace and implement in their countries and regions. When and if this recommendation is accepted, the current disproportionate level of adoption and readiness will be ameliorated and put the built environment on a fast-track for the full implementation of sustainable smart practices. Also, the involvement of BIM users in green projects and deployment of BIM technologies to facilitate the adoption of sustainability practices is relatively low in the built environment (Kim & Yu, 2016b; Olawumi & Chan, 2019d). Also, there is a significant lack of awareness of these concepts by the critical stakeholders in the construction projects, and it has thus affected their ability to collaborate towards implementing sustainable smart practices in the built environment. Without addressing these significant barriers, the built environment might not be able to apply these innovative practices; hence, there is the need for construction organizations to 153 empower their staff by ensuring they stay abreast of knowledge and practice regarding sustainable smart practices. More so, construction firms should strategize and restructure their company towards easing the implementation and deployment of BIM and sustainability practices in their organizations. Also, professional bodies such as the RICS, CIOB, etc. should encourage their members and prospective members to attend seminars and workshops that will aid their knowledge and technical knowhow on these concepts. Meanwhile, the research findings revealed there is currently uncertainty and inaccuracy in the assessments of projects using existing green rating systems. More so, there is a lack of uniformity in the sustainability criteria and priority given to each sustainability criteria by the existing rating tools, which are militating against the adoption of sustainable smart practices in these countries. These findings correspond with the previous studies such as Ali and Al Nsairat (2009) and Illankoon et al. (2017). These barriers are still very salient in the built environment, although some leading green rating system such as BREEAM and LEED are attempting to deploy their custom-made rating tool to other countries apart from the originating regions. However, most countries in South America, Asia-Pacific Region, Africa, and some parts of Europe are yet to have a building rating system suited to the local context of these countries. Hence, this study recommends for each country to establish their own custom-made rating systems tailored to their local context of their regions as well as establish their individual green building councils to monitor the progress of the implementation of sustainability practices in their building projects. 5.7 Conclusions and recommendations This study identified and evaluated the key barriers to the implementation of smart, sustainable practices which was the primary research aim of this paper. A total of thirty-eight barrier factors were identified via a desktop literature review and the factors outlined in a questionnaire which was ranked by 220 respondents from 21 countries who participated in the international survey and have direct and extensive experience in smart, sustainable practices. The survey participants came from diverse professional disciplines and organizational backgrounds, which further lend credence to the data collected. The study meanwhile conducted a comparative assessment of the perceptions of the study participants based on their professional 154 disciplines and organizational backgrounds towards establishing patterns of difference. A significant finding of this study is that there is a relative level of agreement among most of the groups of respondents on factor BA8- “inadequacy of requisite experience, knowledge, and skills from the workforce” as a critical impediment to the implementation of smart, sustainable practices in the built environment. The research findings also revealed that the architects perceived the longer time required for them to learn and adapt to new technologies as the most significant barriers. Even, the academics disagreed with the perception of the practitioners that factor BA11- “low level of research in the industry and academia” is highly significant. On the other hand, the academics opined that there is a considerable increase in the level of research in these concepts in universities, and this perception is consistent with the recent findings in the literature. Another profound research finding, is the classification of the critical barriers or impediments via factor analysis of the thirtyeight barrier factors yielded seven clusters with a minimum of three factors in each cluster and a maximum of six factors; while each factor cluster was given an identifiable and collective label to represent its sub-set factors. After examining the perceptions of the diverse groups of the survey respondents, some useful recommendations and effective strategies for mitigating or eliminating the barriers are suggested. These recommendations include: (1) Professional bodies and construction firms should engage more in the training of their members and staff through the mediums of training workshops and knowledge seminars; (2) Increase in funding support to aid the adoption of smart, sustainable practices; (3) Provision of government subsidy to ease the ‘financial stress’ of small and medium scale construction firms; (4) Incorporating smart, sustainable practices in the curriculum of construction-related colleges and departments; (5) The need for construction firms and stakeholders to be proactive in adopting new and innovative concepts; (6) The development of effective strategies and plans for fast-tracking the implementation of smart, sustainable practices by construction organizations; and (7) The need to ease the access to and exchange of relevant data among project stakeholders. An obvious limitation of this study is that only BIM out of the several smart technological tools was examined as it influenced sustainability practices. The justification for this has been provided in Chapter 1, sections 1.3.2 and 1.3.6 for perusal. 155 The study has qualitatively and quantitatively evaluated the impediments and barriers to smart and sustainable practices in the built environment. The ranking of the key barriers or impediments can form a sound basis for developing the practical and well-informed decision-making process by government departments and construction stakeholders. The research findings have contributed to the existing body of knowledge on sustainability and the use of smart technologies to aid the implementation of concepts in the built environment by determining the key barriers to and providing practical recommendations for the implementation of smart, sustainable practices. The findings can be adopted as a policy instrument and useful guidelines for government agencies, stakeholders, and others towards ensuring BIM can be used to deliver the full potential of sustainability practices in the construction industry. The implementation of the findings of this study is imperative as it will enhance the capacity of the built environment to maximize the perceived benefits of smart and sustainable practices in its everyday activities. Meanwhile, if policymakers and other key stakeholders consider these significant barriers as identified and classified in this study; it is hoped that these challenges can be overcome or eliminated. Collaborative efforts from policymakers, local authorities, practitioners, academics, and other key stakeholders can help to combat these challenges. It is envisaged that the research findings have stimulated multitudinous open debate for reference to the underlying problems besetting the built environment in each local context and internationally. 5.8 Chapter Summary This chapter examines the major barriers to the application of smart tools to enhance the implementation of sustainability practices in the built environment. The study collated 38 types of impediments from a comprehensive desktop review of the literature, and the data collected were further subjected to expert review via the use of empirical questionnaire surveys. The perceptions of 220 professional respondents from 21 countries were collated via the surveys for statistical analysis and classification purposes. The study findings revealed the significant impediments as related to inadequate knowledge and skills, the current market structure and inherent resistance to change in the built environment, and organizational challenges, among others. A comparative analysis of the perceptions of the diverse groups of survey 156 participants was conducted and discussed. The adoption of the survey findings is envisaged to help the built environment in minimizing the impact of these barriers and can serve as a policy instrument and useful guidelines for government agencies, stakeholders, and others towards ensuring BIM can be used to deliver the full potential of sustainability practices in the construction industry. The study has provided effective practical strategies and recommendations for enhancing the implementation of smart sustainability practices in the built environment. The following chapter identifies and analyzes the key drivers or critical success factors of the implementation of BIM and sustainability practices – otherwise known as smart sustainable practices in this study – in building projects and in the built environment. 157 CHAPTER 6: KEY DRIVERS OF IMPLEMENTING SMART AND SUSTAINABLE PRACTICES IN THE BUILT ENVIRONMENT6 6.1 Chapter Overview The previous chapter identified and analyzed the impediments to the implementation of smart and sustainable practices – BIM and sustainability – in building projects and the built environment. This chapter identifies and examines the key (critical success factors) of the implementation of BIM and sustainability practices – otherwise known as smart sustainable practices in this study – in building projects as well as in the built environment. Two datasets were collected namely via Delphi surveys (14 experts participating) and international survey involving 220 respondents. However, to conserve space, only the result of the international survey findings will be presented in this chapter. A link to the published findings of the Delphi survey conducted in this research is provided in section 6.4 of this chapter. 6.2 Introduction Construction projects are nowadays quite complex involving several and interwoven processes and activities (Olatunji et al., 2017b; Olawumi & Ayegun, 2016) which calls for a smart and innovative system of technologies to process and manage the different project activities. Also, the Brundtland Commission report (WCED, 1987) has drawn the attention of the construction sector to implement sustainable construction practices in its activities to enhance the environmentally-friendliness of its products (infrastructures, buildings) with the ultimate aim of achieving sustainable smart cities. Olawumi et al. (2017) regarded Building Information Modelling (BIM) as one of the smart technologies available to the construction industry along with radio-frequency identification (RFID), augmented reality which can help to facilitate collaboration among project stakeholders/ Also, it can serve as links to connect domain knowledge areas such as sustainability, facility management, safety, project management, etc. This chapter is largely based upon the following published papers: Olawumi, T.O., & Chan, D.W.M. (2020c). Key Drivers for Smart and Sustainable Practices in the Built Environment. Engineering, Construction, and Architectural Management, 27(6), 1257–1281. https://doi.org/10.1108/ECAM-06-2019-0305 Olawumi, T.O., & Chan, D.W.M. (2019d). Critical Success Factors of Implementing Building Information Modelling (BIM) and Sustainability Practices in Construction Projects: A Delphi Survey. Sustainable Development, 27(4), 587–602. https://doi.org/10.1002/sd.1925 6 158 to ensure a one-source management of project’s information and processes throughout the construction project’s lifecycle stages. Also, Olatunji et al. (2016b) highlighted further that the success of these tools hinges more on the initiatives of project stakeholders through their decision-making process and collaboration in their projects. According to Kovacic et al. (2015), Lee and Yu (2016), and Ma et al. (2018), there has been an appreciable increase in BIM adoption in some countries’ construction industry. However, despite this progress in BIM adoption (although not yet worldwide), there has been little advancement in the implementation of sustainable construction practices in infrastructural projects. Morlhon et al. (2014) argued nonetheless that the implementation of BIM is complicated due to the different standards and protocols involved which has hindered organizations to use and handle it actively. However, despite this apparent disadvantage, it permits the additional analyses of concepts such as energy performance, clash detection, and other sustainability measures (Olawumi et al., 2017). Also, BIM-enabled sustainability analysis tools can assist in the simulation of building energy performance and carbon footprints as well as reduce the cost and time involved (Ahn et al., 2014). Although, the interoperability issues between BIM design and analysis tools is still a prevalent problem in the construction industry (Abanda et al., 2015). The integration of smart technologies such as BIM to amplify sustainability practices in construction projects can help to reduce and/or project the building energy as well as the evaluation of the lifecycle assessment in conjunction with rating systems such as LEED, BREEAM, etc. (Al-Ghamdi & Bilec, 2015). Sustainability is related to dimensions such as social, economic and environmental variables; and the use of technologies can optimize its adoption in any setting (Raut et al., 2018). More so, adopting sustainability strategies can lead to innovation which can also help to achieve competitive advantage for participating companies and reduce project overall cost (Chofreh & Goni, 2017). The deployment of cloud technologies also facilitate collaboration and improved the project governance mechanisms (Alreshidi et al., 2016). However, the lack of archival data and access to vital project information have steeped the progress and adoption of BIM in the industry (Wong et al., 2014). A case study analysis of the benefits of BIM in construction project carried out by Barlish and Sullivan (2012) showed that BIM is yet to achieve its full potential 159 in the industry due to several factors; which include the lack of commitment from project clients. Moreover, previous authors (GhaffarianHoseini et al., 2017; Jalaei & Jrade, 2015; Reinhart & Wienold, 2011) sees smart technologies such as BIM as one of the essential vehicles to drive the implementation of sustainability practices. Meanwhile, Olawumi and Chan (2019e) developed an assessment template and scoring system to provide a quantitative metrics of measuring and comparative evaluation BIM implementation in developing countries Hence, this study intends to identify and examine the critical drivers that amplify the use of BIM to enhance the implementation of sustainable practices in construction projects. Gardas et al. (2018a) and Raut et al. (2017) considered issues related to sustainability in any industry as a concept that is best implemented by top hierarchy of organizations, and such firms derives the benefits of its contribution to sustainable development in aspects such as economic, social, environmental. Guo et al. (2018) and Xue (2018) corroborated it by arguing for greater leadership and the institutionalization of a governance arrangement in the industry. Gardas et al. (2018b) emphasized the need for a holistic view and balancing of the three pillars of sustainability during its implementation process. More so, Jakhar (2017) and Kang (2018) regards communication and stakeholder engagement as a crucial variable in facilitating sustainable development. Given the above, this study aims to assess the key drivers (KDs) that aid the implementation of smart and sustainable practices in the construction industry and projects. More so, the following research questions will be answered in towards achieving the aim of the study: i. What are the significant drivers that can aid the implementation of smart and sustainable practices in construction projects? ii. How do the perceptions of the study’s respondents differ based on their professions, organizational setups, and regions? iii. What are the practical implications of the study’s findings on the implementation of smart and sustainable practices in the built environment? Although, there are some projects which have employed either of the two concepts to varying levels of success. However, this study focuses on construction projects in 160 which the clients or project team plans to adopt smart technologies such as BIM along with sustainable practices in their projects. The comparative evaluation of the perceptions of the respondents based on their professions, organizational setup, and regions is expected to shed more insight and perspectives on the implementation of smart and sustainable practices in construction projects, firms, and regions. The findings are also expected to enhance the capacity of project stakeholders, professional bodies, government agencies to implement BIM and sustainability practices in their projects and locality. Cugurullo (2017) argued that several models of urbanization of cities are flawed because little attempts are made to integrate sustainability into its planning and design. Hence such urban development becomes unsustainable in the long-run. A review of case studies by previous authors (Caprotti, 2016; Taylor Buck & While, 2017) reveals the disconnect between the development of smart cities and the ideals of sustainability. One significant disconnection between the two concepts as argued by Cugurullo (2017) is that there is little or no innovation but a rather replication of traditional strategies of urbanization. Also, as pointed out by extant literature (Chang & Sheppard, 2013; Colding & Barthel, 2017; Datta, 2015b) they hardly integrate sustainability or fulfill its promises of making it sustainable. Conceptually and in practice, it has been seen that the use of smart technologies in construction project development to achieve smart city initiatives may not act in concert with the ideas of adopting sustainability to achieve eco-city. A good case analysis was exemplified by Cugurullo (2017) in the comparison between Hong Kong (a smart city) and Masdar City, Abu Dhabi (an eco-city initiative). Given these limitations in knowledge and practice, the current study intends to break the ‘aura of singularity’ in the application of these concepts to city urbanization by projecting the possibility of sustainable smart cities and buildings that works on the principles that a smart city and building can be sustainable and an eco-city or green building can be smart. The study argues in favor of sustainable smart buildings as against the singularity of either the advancement of smart-building or green builidng initiatives. This study reiterated the need for the application of BIM and sustainability practices in construction projects as against the singularity of the adoption of either BIM or sustainability practices initiatives. Some studies (Mom et al., 2014a; Tsai et al., 2014a) have examined the adoption of BIM in construction projects. However, they 161 focused solely on BIM implementation. Hence, this study will bridge the gap by identifying key drivers that can aid the joint implementation of BIM and sustainability practices in construction projects. 6.3 Smart and sustainable practices: Salient issues in the built environment The built environment has witnessed an increased knowledge and adoption of innovative concepts and processes which were intended to enhance the overall construction process, improve productivity, among others. Some of these concepts include sustainability (Lozano, 2008; Olawumi & Chan, 2018a); risk management (Xu et al., 2010), safety management (Zhang et al., 2015); BIM (Qi et al., 2014) among others. According to Albino et al. (2015) and Olawumi and Chan (2018a), the concept of smart buildings and sustainability has gained enormous recognition in the literature, government circles, and from international organizations. The nexus between BIM and sustainability issues which gave rise to the concept of smart sustainable practices as discussed in section 6.2 is found in the capacity of BIM system to embed a large amount of data for storage, document management, communication among stakeholders, visualize sustainability analyses results, etc. (Gu & London, 2010; Olawumi et al., 2017). However, despite the increasing adoption of BIM in the construction projects, Kassem et al. (2012) and Olatunji et al. (2017b) stressed that the difficulty in evaluating the business value of smart tools like BIM in terms of return on investment (ROI) has hindered its implementation in construction projects especially in small and medium scale projects (capital-wise). Hence, per Alsayyar and Jrade (2015), to improve its implementation, it is important to provide anecdotal evidence of profitable deployment of BIM in construction projects to the prospective clients to increase their satisfaction and confidence. BIM is described as a system that consists of its product and processes (Olawumi & Chan, 2019e, 2019b). The incorporation of smart tools such as BIM in sustainability issues is aimed to serve as a decision-making tool when integrated with the existing building rating systems to evaluate the level of achievement of some sustainability criteria by buildings (Ahvenniemi et al., 2017). Previous studies have examined the application of BIM for improving building sustainability. For instance, Lu et al. (2017) reviewed the uses of BIM in green buildings and their capacity to support the building lifecycle stages. Also, Lu et al. 162 (2017) and Olawumi et al. (2017) highlighted some BIM functionalities in enhancing building sustainability such as design analysis to evaluate energy performance and carbon emission analyses, daylighting analysis, sustainable material selection among others. Akinade et al. (2015) and Olawumi and Chan (2018d) also discussed some benefits of integrating smart and sustainable practices in construction projects to include – (1) enhancing the productivity and efficiency of construction projects (Gu & London, 2010); (2) real-time sustainable design analysis and simulation (Kivits & Furneaux, 2013); (3) minimize carbon emission and footprints (Hope & Alwan, 2012); and (4) improving building energy efficiency (Boktor et al., 2014; Harding et al., 2014) among others. However, despite all these benefits derivable from implementing smart sustainable practices in construction projects; Marsal-Llacuna et al. (2015) revealed that project stakeholders tend towards the sole adoption of BIM more than implementing the two innovations. Meanwhile, to boost the adoption of smart and sustainable practices in construction projects, Aibinu and Venkatesh (2014) and Nanajkar and Gao (2014) recommended for developers of BIM tools to focus more on suitable cloud-based technology and open-source software. In a similar vein, Ahvenniemi et al. (2017) stressed the importance of smart tools to be cost and resource-efficient. Meanwhile, Becerikgerber and Kensek (2010), Olawumi and Chan (2019d), and Sackey et al. (2015) observed that the involvement of project teams in the early stages of the construction project could enhance its adoption in such construction projects. Project complexity in terms of its shape and system can also pose challenges to the adoption of smart and sustainable practices due to instances of incomplete and unreliable information in building models (Aksamija, 2012; Olawumi et al., 2017; Peansupap & Walker, 2005). Also, Rogers et al. (2015) argued that the lack of industry standard for BIM and sustainability assessment is one of the banes for the slow progress in the adoption of smart sustainable practices in some countries. Also, the existing green building rating tools, according to Berardi (2013) and Robinson and Cole (2015), places greater consideration on the environmental aspect of sustainable development instead of a holistic consideration of the three sustainability pillars. Towards ameliorating this significant gap in the literature, Olawumi et al. (2018) recommended for these green rating tools to embed other aspects of sustainability- economic and social pillars in their evaluation of building 163 sustainability. Also, Huang et al. (2009) reported that some of the sustainability criteria used in evaluating building sustainability do not reflect its actual interaction with the urban system nor provide indications on the strategies to deploy to achieve these criteria. Another salient issue regarding the implementation of smart and sustainable practices in the built environment is the legal issues regarding their use and ownership. Aranda-Mena et al. (2009) and Azhar (2011) advocated for the development of a uniform legal framework and practice to resolve the problem of proprietary ownership of BIM models, simulation results and, contractual issues, and project uncertainties among others. Therefore, to ensure an industry-wide implementation of smart and sustainable practices in the built environment, Aibinu and Venkatesh (2014) and Redmond et al. (2012) recommended for local authorities and government agencies to set out policies and legislation for its deployment and enforcement of relevant guiding laws and statues. The establishment of good working practice and strategy to aid the implementation of smart sustainable practices cannot be over-emphasized (Azhar, 2010). Jung and Joo (2011) recommended the development of standards that can enhance the effectiveness of the adoption of BIM (Jung & Joo, 2011; Olawumi & Chan, 2019b, 2019e) and sustainability practices (Olawumi & Chan, 2018a) in the built environment. Meanwhile, vital support of construction firms’ top management is critical to the continuous and successful implementation of these innovative concepts in the construction industry (Boktor et al., 2014; Saxon, 2013). Also, the firm’s leadership support can be in diverse forms- such as financial supports, redesign of the firm’s structure and policy to suit the new concept, and training supports among others (Chan et al., 2019a). Cugurullo (2017) acknowledged the quest by some cities such as Masdar City in Abu Dhabi to be an eco-city project and Hong Kong as a smart city among others. However, it resulted in an uneven pattern of urban development because of the singularity of the adoption of either smart tools or sustainable practices. Hence, it is important to consider both concepts – BIM and sustainable practices; and one of the ways to achieve this is to investigate the key drivers that can enhance the adoption of smart and sustainable practices in construction projects. Given the above, Table 6.1 shows the summary of the KDs that can enhance the execution of smart sustainable practices as identified via a review of extant literature 164 and through pilot studies. The 30 KDs highlighted in Table are sourced based on the scope definition of this study, as discussed in section 6.2. Subsequent sections of this paper will define the adopted research methodology adopted and examine the perceptions of over 200 survey respondents whose perceptions formed the basis of the identification of the significant KDs of implementing smart and sustainable practices in the built environment. Table 6.1: Summary of identified KDs for the execution of smart sustainable practices Code Key drivers References C1 “Technical competence of staff” C2 “Greater awareness and experience level within the firm” C3 “More training programs for cross-field specialists in BIM and Sustainability” “Increased research in the industry and academia” “Government establishment of start-up funding for construction firms to kick-start BIM initiatives” “Adequate construction cost allocated to BIM” C4 C5 C6 C8 “Availability of financial resources for BIM software, licenses, and its regular upgrades” “Information and knowledge-sharing within the industry” C9 “Effective collaboration and coordination among project participants” C10 “Establishment of a model of good practice for BIM and sustainability execution” “Availability and a well-managed in-house database of information on similar projects” C7 C11 C17 C18 C19 C20 “Development of appropriate legal framework for BIM use and deployment in projects” “Security of intellectual property and rights” “Shared risks, liability, and rewards among project stakeholders” “Establishment of BIM standards, codes, rules, and regulations” “Appropriate legislation and governmental enforcement & credit for innovative performance” “Increased involvement of project stakeholders in green projects” “Clarity in requirements and measures for achieving sustainable projects” “Number of subcontractors experienced with BIM projects” “Client requirement and ownership” C21 C22 C23 C24 “Early involvement of project teams” “Client satisfaction level on BIM projects” “Supportive organizational culture and effective leadership” “Project complexity (regarding building shape or building systems)” C25 “Availability and affordability of cloud-based technology” C26 “Interoperability and data compatibility” C12 C13 C14 C15 C16 165 Gu and London (2010); Tsai et al. (2014a); Deutsch (2011) Chan (2014); Kassem et al. (2012) Wong and Fan (2013); Jalaei and Jrade (2014) Abdirad (2016); Bolgani (2013) Abubakar et al. (2014) Gu and London (2010); Kivits and Furneaux (2013) Nanajkar and Gao (2014) Azhar (2011); Chan et al. (2019b) Antón and Díaz (2014); Hanna et al. (2014) Antón and Díaz (2014); Adamus (2013) Aibinu and Venkatesh (2014); Becerik-gerber and Kensek (2010) Aibinu and Venkatesh (2014); Azhar (2011) Kivits and Furneaux (2013) Chan (2014); Park et al. (2013) Redmond et al. (2012) Antón and Díaz (2014); Hope and Alwan (2012) Alsayyar and Jrade (2015) Aibinu and Venkatesh (2014) Chan (2014) Ahn et al. (2014); Chan et al. (2019a) Kassem et al. (2012) Ahn et al. (2014); Chan (2014) Yeomans et al. (2006) Hope and Alwan (2012); Kivits and Furneaux (2013) Ahn et al. (2014); Yeomans et al. (2006) Adamus (2013); Saxon (2013) Code C27 C28 C29 C30 Key drivers References “Standardization & simplicity of BIM and sustainability assessment software” “Technical support from software vendors” “Availability of BIM and sustainability databases” “Open-source software development” Akinade et al. (2017); Aksamija (2012) Redmond et al. (2012) Abolghasemzadeh (2013); Antón and Díaz (2014) Hope and Alwan (2012) Note: The key drivers were modified from Olawumi and Chan (2019d). 6.4 Research Methodology The study identified and assessed the key drivers that aid the key construction stakeholders in their execution of smart sustainable practices in the construction study. A quantitative research method was adopted in this study using an empirical questionnaire survey and secondary means of data such as the desktop review of relevant journal articles, online materials, textbooks, official gazettes, and building standards, etc. As pointed out by Olatunji et al. (2017a), the means and instruments of data collection are essential to the achievement of the study’s aim and reliability of the collated data. Hence, the use of the empirical questionnaire survey in this study helps to aggregate the opinions of stakeholders in the built environment as regards the 30 KDs. Although opinions of respondents might be subjective based on their experience, locations, etc. the use of several statistical methods helps to minimize these biases. The targeted survey respondents for the questionnaire were sampled via using both purposive and snowball sampling techniques, and they have requisite direct handson experience in smart digital technologies like BIM and the process of achieving sustainability in building projects. Three delivery modes were used in the questionnaire distribution, which is yielded 220 responses from 21 countries as follows: (1) online survey (161 responses), (2) fill-in PDF questionnaires (14 responses), and (3) hand-delivered questionnaires (45 responses). Some of the respondents were sent both the online survey link and an attached PDF survey form. Also, before the survey form distribution, the survey form was pretested. The weblinks to the online survey form and the fill-in PDF survey was posted on relevant LinkedIn groups of different professionals in the built environment, ResearchGate, network groups, email addresses culled from webpages of universities, professional bodies, construction companies, etc., among others social media means. The 166 respondents were told to input their contact details if they are interested in the final result of the survey, which is intended to serve as a motivation for the respondents to participate in the survey exercise. Although the survey exercise yielded responses from 21 countries, no countries, in particular, were targeted. The main goal of the questionnaire distribution is to secure a good representative number of responses from each region of Europe, Asia, Africa, and North & South America. The respondents were also encouraged to share the questionnaire survey link to their colleagues with requisite knowledge of the subject matter. The first section of the survey form (see Appendix B) solicited basic information about each survey participant (such as their profession, years of experience in the construction industry, their organization type, location, and awareness of BIM and sustainability concepts) and the other sections request the respondent to rate the KDs on a 5-point Likert scale (1 = strongly disagree, 3 = neutral and 5 = strongly agree). If a factor is not perceived to be applicable as a CSF, the respondent has the option to tick an ‘N/A’ box. The gleaned data were analyzed using various statistical methods as explained in the next sub-section and the findings discussed in subsequent sections of this paper. Meanwhile, prior to the empirical international questionnaire survey, a Delphi survey was conducted involving 14 experts across eight countries towards reaching a consensus on the cross-field research topic. See Olawumi and Chan (2019d) for the published findings of the Delphi survey exercise. 6.4.1 Statistical methods and reliability tests Inferential and descriptive statistical tools were adopted to evaluate the set of data collated from the study’s respondents. These tools included: (1) Reliability using the Cronbach’s alpha (α) reliability test; (2) Ranking via mean scores (M) and standard deviations (SD); (3) Analysis of variance (ANOVA), post-hoc Tukey tests, correlation analysis; and (4) Factor analysis and clustering. According to Field (2009), Olawumi et al. (2018) and Olawumi and Chan (2018d), a set of data must undergo reliability testing to evaluate whether the data instruments are measuring the right construct (Olatunji et al., 2017a; Olawumi, 2016). The Cronbach alpha (α) is useful to measure whether the questionnaire and its associated Likert scale measures the right construct and maintains an internal 167 consistency (Field, 2009; Saka et al., 2019b) value for the study was 0.966 which is significantly higher than 0.70, the minimum threshold for a reliable dataset (Olawumi & Chan, 2019d). This implies that the dataset has good internal consistency, reliable, and suitable for further statistical analysis (Chan et al., 2010; Chan & Choi, 2015). Therefore, for the KD’s factor ranking, if there is a case of more than one factor having the same mean value, their SD values will be utilized in ranking them; such that, those with lower SD values are ranked higher (Olatunji et al., 2017a). However, in the case of the factors having the same mean score and SD value, they will be accorded the same factor ranking (Olawumi et al., 2018). 6.4.2 Respondents’ demographics A diverse group of 220 survey participants across 21 countries participated in the study (see Figure 6.1). The respondents are from six varied set of organizational setups as classified in this as follows – academics (40%), public sector clients (25%), main contractors (16%), project consultants (11%), private sector clients (5%), and property managers (3%). It must be noted that personal information, such as the names of their organization or firm, were not solicited from the respondents in the survey form. Hence, the respondents could not be grouped by such means. The respondents were also classified based on their profession, and the results revealed that the quantity surveyors, architects, and project managers were more represented in the study’s respondent population with a percentage of 25, 12.7, and 12.3 respectively. The civil engineers (11%) and building services engineers (8%) followed closely. The distribution of the respondents (see Figure 6.1) based on their regions are Asia (56.4%), Africa (29.1%), Europe (9.1%), and America (5.5%). The key countries based on the number of participating respondents in the Asia region are China, Singapore, and Australia; in Africa, we have Nigeria, South Africa, and Egypt. For the European continent, we have the United Kingdom and Germany; and in the American regions (South and North America), we have the United States and Canada. The respondents have a high level of knowledge and awareness of BIM and sustainability practices with 43% and 53% respectively, while about 37% and 36% of the respondents reported an average level of understanding of BIM and sustainability respectively. Further analysis of the level of awareness of the respondents based on their knowledge of BIM revealed that Europe (70%) and the 168 America regions (67%) have more than two-thirds of their respective respondents’ population with at least a high level of awareness of the BIM process. Meanwhile, Africa and Asia have 47% and 35% respectively with at least a high level of BIM awareness. The findings correspond with the extant literature (Jung & Lee, 2015; Olawumi et al., 2017) which examines the adoption of BIM across various regions. For the sustainability practices awareness, regions such as Africa (67.2%), America (66.7%), and Europe (60%) have more than two-thirds of their respective respondents’ population with at least a high level of awareness of the sustainability practices. The respondents from the Asia region have 42.7% with at least a high level of awareness of the sustainability practices. This analysis corresponds with the extant literature (Olawumi & Chan, 2018a) which discusses the trend and implementation of sustainability in different regions and countries. Figure 6.1: Respondents' demographics The demographics of the respondent (Figure 6.1) based on their level of experience was also evaluated. On average, 44.5% of the respondents have at least 11 years of 169 working experience in the construction industry, of which about 23.6 percent have more than 20 years of working experience. The opinions of the respondents were also solicited on which stage of the project development to implement smart sustainable practices; 57% and 37% of the respondent considers the planning and design stages respectively, while only 6% preferred the construction phase. The result of the statistical analysis of the respondents’ demographics revealed that the professionals which supplied the necessary data upon which the study’s findings are based have a mixture of both practical experience and theoretical knowledge in the subject matter. Hence, this lends further credence to the data collected and subsequent analysis in this study. 6.5 Results of statistical analyses This section expatiated on the results of the gleaned data via the survey forms and analyzed using various statistical methods and discusses the survey findings. 6.5.1 Descriptive statistical tests In ranking the key drivers based on the data collected from the study’s respondents, the mean score “M” and standard deviation “SD” was employed. In situations where two or more KDs have the same mean value, their SD values are considered in the ranking as highlighted in Olatunji et al. (2017a) and Olawumi and Chan (2018d) The mean scores for the 30 identified individual KDs range from M= 3.79 (SD= 0.919) for “C25 - availability and affordability of cloud-based technology” to M= 4.34 (SD= 0.780) for “C1 - technical competence of staff” at a variance of 0.55 (see Table 6.2). A benchmark score of 4 out of 5 on a 5-point Likert scale was used in the study to identify the highly significant KDs of smart sustainable practices in the construction industry. Using this approach, the study pinpointed top-five KDs which include: “C1 technical competence of staff” (M= 4.34, SD= 0.780), “C3 - more training programs for cross-field specialists in BIM and Sustainability” (M= 4.27, SD= 0.738). “C21 early involvement of project teams” (M= 4.24, SD= 0.821), “C2 - greater awareness and experience level within the firm” (M=4.22, SD= 0.728), and “C9 - effective collaboration and coordination among project participants” (M= 4.17, SD= 0.784). The findings revealed that to enhance the execution of smart sustainable practices in the construction industry, it rests on the technical competency and knowledge of the project stakeholders on BIM and sustainability. Also, proper coordination and early 170 involvement of project team members are very significant (Antón & Díaz, 2014; Olawumi & Chan, 2018c). Hence policymakers, local authorities, and other key stakeholders need to prioritize human capital development in their drive for the adoption of smart technologies and implementation of green buildings. There is a considerable agreement among all the respondents’ groups on factor “C3 - more training programs for cross-field specialists in BIM and sustainability” (Olawumi et al., 2018; Wong & Fan, 2013); which was ranked as a key factor and rated among the top five most important factor by all the groups. The finding reveals that when stakeholders in the construction industry have considerable knowledge and skillset in smart sustainable practices, it will ease its execution in the built environment. Also, for factor “C1- technical competence of staff” (Aibinu & Venkatesh, 2014); which was ranked the most significant driver for the execution of smart sustainable practices; the factor was ranked among the top-five key factors by all the respondent’s groups except the private clients and academics’ groups who both gave it the 7th rank. The result aligns with the findings of Olawumi and Chan (2018c) who recommended to the government and professional bodies in the construction industry to organize regular training workshops and seminars to keep their staff and members abreast of the current trend in the industry and equip them with necessary technical skills as required. The differing rank by the academics and private clients is consistent with the fact that academics are the knowledge of the industry and the private clients generally have the resources to train their staff, although their rankings are still relatively above the average. Meanwhile, for the factor “C21- early involvement of project teams” (Goedert & Meadati, 2008), the perception of the project consultants and respondents from the main contractors differs significantly from other survey participants from other organization-based respondents. The two respondent’s groups ranked the factor as 10th rank as against the top-five rankings achieved by the factor in other respondents’ groups. The findings reveal an average recognition of the fact that the early introduction of key stakeholders at the planning stage of a project could influence the achievement of smart sustainable practices in the project. This is because most consultants to the project are primarily involved in the project from its start. However, several issues which vary from poor coordination and collaboration, and difficulty in analysis the sustainability credentials of building plans at the early 171 phase of project development has contributed in a way to hinder the smooth execution of such innovative strategy. Table 6.2: Key drivers for the execution of smart sustainable practices in the construction industry: inter-group comparisons KDs Architects Civil Engineers Project Managers Building Serv. Engr. Constr. Managers Overall C1 Mean 4.64 Rk 2 Mean 4.33 Rk 8 Mean 4.20 Rk 1 Mean 4.20 Rk 3 Mean 4.48 Rk 1 Mean 4.34 SD 0.780 Rk 1 F 1.137 Sig. 0.336 C2 4.54 3 4.00 28 3.84 24 3.77 31 4.03 26 4.22 0.728 4 1.040 0.411 C3 4.64 1 4.08 23 3.88 22 4.03 15 4.39 4 4.27 0.738 2 1.770 0.068 C4 4.21 18 4.25 16 4.04 8 3.91 24 4.34 7 4.05 0.803 13 2.491 0.008 C5 4.11 23 4.17 19 3.76 30 3.86 27 3.89 32 4.01 0.917 19 1.230 0.273 C6 4.00 29 4.17 19 3.92 19 3.86 28 3.72 30 3.87 0.898 28 0.968 0.473 C7 4.36 10 4.33 8 3.96 16 3.97 21 3.89 31 3.98 0.924 22 1.280 0.243 C8 4.32 15 4.25 16 4.04 7 4.09 10 4.48 2 4.07 0.805 10 1.117 0.351 C9 4.39 8 4.50 3 3.92 17 3.86 29 4.20 13 4.17 0.784 5 0.929 0.507 C10 4.36 12 3.92 31 4.12 3 3.74 32 4.17 19 4.11 0.783 7 1.308 0.228 C11 4.32 15 3.50 36 3.28 30 3.49 36 3.76 34 4.01 0.791 18 1.037 0.413 C12 4.11 22 4.00 28 3.64 33 3.69 33 3.99 30 4.03 0.807 15 1.049 0.403 C13 4.00 28 3.67 35 3.64 34 3.63 34 3.74 35 3.87 0.884 27 1.809 0.061 C14 4.07 26 3.92 31 3.44 35 3.54 35 3.85 33 3.94 0.823 24 1.017 0.430 C15 4.43 6 4.33 8 4.08 5 4.17 5 4.34 8 4.15 0.820 6 1.564 0.119 C16 4.39 9 4.25 15 3.96 13 4.17 5 4.18 15 3.99 0.833 20 2.035 0.031 C17 4.21 19 4.42 5 3.92 19 4.14 8 4.41 3 4.02 0.785 17 0.989 0.454 C18 4.14 21 4.33 12 3.92 17 4.03 14 4.31 10 4.03 0.746 14 0.585 0.825 C19 4.00 27 3.75 34 3.96 14 4.06 13 4.02 28 3.93 0.805 25 0.825 0.604 C20 4.00 30 3.83 33 4.00 9 4.09 10 4.02 28 3.98 0.883 21 1.031 0.419 C21 4.46 5 4.08 24 3.96 14 4.26 1 4.20 14 4.24 0.821 3 1.140 0.334 C22 4.11 24 4.08 26 4.08 6 4.23 2 4.34 9 4.05 0.801 12 0.752 0.675 C23 4.39 7 4.00 30 3.84 24 3.89 26 4.08 25 4.07 0.782 9 1.433 0.168 C24 4.11 25 4.17 19 4.00 9 4.11 9 4.16 20 3.91 0.879 26 1.494 0.143 C25 4.21 20 4.25 14 4.00 9 3.91 23 4.21 12 3.79 0.919 30 1.754 0.071 C26 4.36 10 4.42 5 3.80 26 4.00 17 4.18 17 4.06 0.817 11 1.608 0.106 C27 4.36 12 4.00 27 3.88 22 3.89 25 4.10 23 4.08 0.843 8 1.535 0.129 C28 4.36 12 4.42 5 3.80 26 3.83 30 4.14 21 3.95 0.890 23 2.664 0.004 C29 4.50 4 4.17 19 3.72 31 4.06 12 4.18 18 4.03 0.883 16 1.394 0.185 C30 4.25 17 4.17 18 3.76 29 3.94 22 4.03 27 3.86 0.926 29 1.591 0.111 Note: Rk = Rank 172 6.5.2 Inferential statistical tests Parametric statistical methods such as ANOVA test were applied to the collated data to investigate any discrepancies in the perceptions of the different groups of survey participants such as organizational setups (e.g. public and private clients, project consultants, main contractors, etc.) and those categorized based on their professional disciplines (e.g. architects, civil engineers, project managers, etc.). ANOVA test is a parametric tool which measures variance using the mean of scores (Olawumi & Chan, 2019d; Tsai et al., 2014a); and according to Mom et al. (2014a) and Olatunji et al. (2017a), if a factor is significant (p<0.05); a post-hoc Tukey test will be conducted. Moreover, before an ANOVA test can be performed on a set of data, the assumption of homogeneity of the sample data must be satisfied (i.e. p>0.05) which states that that the variance across groups is equal. Levene’s test for homogeneity of variances was employed, and the significance level (p-value) for the KDs was greater than p > 0.05, which implies the group variances are equal. Hence, parametric tests (such as ANOVA) will be useful for further analysis of the data. 6.5.2.1 Statistical tests based on organizational setups The ANOVA test employed on the data (at a significance level <5%) showed no divergence in the perceptions among the groups of respondents based on the organization setups identified in the study. These organizational setups include respondents from the main contractors, academics, public clients, private clients, and project consultants. The findings are consistent with the fact that a good number of the respondents might have been engaged in two or more of these organizational setups in the course of their professional jobs. Also, even those in academics often have a partnership with colleagues practicing in the industry (Olatunji et al., 2017a), and they do share both theoretical and practical experiences. Furthermore, since the concept of smart sustainable practices is an interdisciplinary discipline, there exists a thin line in the workings of the several organizations in the construction industry. 6.5.2.2 Statistical tests based on professional disciplines The ANOVA statistical method conducted on the survey data revealed some significant differences (at a significance level <5%) in the opinions among the survey participants on three KDs (see Table 6.2). These drivers include two factors with significant differences “C4- increased research in the industry and academia” 173 [F(10,209)= 2.491, p=0.008]; and “C28- technical support from software vendors” [F(10,209)= 2.664, p=0.004]. The other factor “C16- appropriate legislation and governmental enforcement & credit for innovative performance” [F(10,209)= 2.035, p=0.031] has a moderate significant difference. However, a further test of the three significant factors via a post-hoc Tukey test revealed a moderate divergence (p=0.036) in only one factor “C16- appropriate legislation and governmental enforcement & credit for innovative performance”; with the architects (M= 4.39, SD= 0.685) perceiving it to be of greater importance than the construction managers (M= 3.58, SD= 0.692). The architects, according to Olawumi and Chan (2019a), are involved early in the construction process when issues relating to smart sustainable practices execution and other concepts are integrated into construction projects. Also, project consultants which include the architects work in conjunction with the clients to ensure the construction project complies with relevant statutory and legal frameworks and standards, which mostly must be adhered to at the planning and design stage of the project. This unique relationship between the client and architect and the fact they are more involved in the early stage of construction projects than their construction managers counterparts, which makes their perception of this factor to be worthy of note. Furthermore, according to Brinkerink et al. (2019), the relevant stakeholders must acquire a good understanding of the applicable legislation which will enable them to develop appropriate plans and strategies to benefit from government subsidies, tax reliefs or other credits for innovation. 6.5.3 Classification of the key drivers based on factor analysis The basic concept of factor analysis is to identify a few numbers of factors that best represent the structure of relationships among a larger set of variables (Olawumi & Chan, 2019a) and aids the illustration of a complex phenomenon (Xu et al., 2010). In the extant literature, two types of factor analysis are prominent, and these include principal component analysis (PCA) and the Promax rotation method (Chan, 2019; Chan & Hung, 2015). The Promax rotation method allows for the underlying factors to be correlated, that is, in a case when the factors are not independent of each other (Chan & Choi, 2015; Chan & Hung, 2015). However, this study adopted the PCA approach as the factors are expected to be independent and also for the unique data-reduction capacity and simplicity of the PCA method (Olawumi & Chan, 2019a). 174 A Pearson correlation analysis was carried out on the 30 KDs and none of the factors correlated to another, thus, satisfying the use of the PCA method. Varimax rotation, an orthogonal factor rotation method, was employed in rotating the 30 underlying factors. Chan and Choi (2015), and Lingard and Rowlinson (2006) posited a provisional requirement that a set of data must meet before it is suitable for factor analysis. An essential requirement is that the sample size of the data and the number of factors must comply with a ratio of 5:1, which was met by this study. This study has 220 responses which are higher than the minimum 150 sample size necessary for factor analysis to be undertaken. Meanwhile, two further tests- Kaiser-Meyer-Olkin (KMO) value and Bartlett’s test of sphericity (BTS) were carried out to test the appropriateness of the dataset for factor analysis. KMO values range from 0 to 1 and measure the relative compactness of correlations among the factors. The KMO value for the study is 0.948 which indicates the PCA generated a reliable and distinct cluster (Chan & Choi, 2015; Xu et al., 2010). The BTS examines the correlation among the underlying factors, and the BTS analysis revealed a chi-square test value of 4,926.376 at a very small significance level (p=0.000, df= 435) which implies that the correlation matrix is not an identity matrix (Xu et al., 2010). As the key prerequirements of factor analysis has been met, PCA can be applied to the dataset, and it also ensures consistent and reliable results. Five clustered factors were generated from the PCA analysis (see Table 6.3) which represents 68% of the total variance explained which is higher than the minimum eigenvalues of 60% (Chan, 2019; Chan & Choi, 2015; Chan & Hung, 2015; Malhotra, 1996). Also, the underlying factors have a factor loading which ranges from 0.459 to 0.797, and the classification of the underlying factors under each cluster is reasonable and sufficient. 175 Table 6.3: Factor structure for the PCA analysis of the KDs Code KDs for implementing smart sustainable practices 15.236 50.786 Cumulative percentage of variance explained 50.786 1.518 5.059 55.845 1.392 4.641 60.486 1.301 4.337 64.823 Factor Eigenvalue loading Factor 1 – Knowledge & industry-related drivers C1 “Technical competence of staff” C3 “More training programs for cross-field specialists in BIM and Sustainability” C9 “Effective collaboration and coordination among project participants” C2 “Greater awareness and experience level within the firm” C11 “Availability and a well-managed in-house database of information on similar projects” C10 “Establishment of a model of good practice for BIM and sustainability execution” C23 “Supportive organizational culture and effective leadership” C8 “Information and knowledge-sharing within the industry” C4 “Increased research in the industry and academia” Factor 2 – Financial, legal & statutory drivers C7 “Availability of financial resources for BIM software, licenses, and its regular upgrades” C6 “Adequate construction cost allocated to BIM” C5 “Government establishment of start-up funding for construction firms to kick-start BIM initiatives” C12 “Development of appropriate legal framework for BIM use and deployment in projects” C16 “Appropriate legislation and governmental enforcement & credit for innovative performance” C15 “Establishment of BIM standards, codes, rules, and regulations” C13 “Security of intellectual property and rights” Factor 3 – Organizational & project-related drivers C22 “Client satisfaction level on BIM projects” C24 “Project complexity (regarding building shape or building systems)” C25 “Availability and affordability of cloud-based technology” C21 “Early involvement of project teams” C26 “Interoperability and data compatibility” Factor 4 – Technical drivers C30 “Open-source software development” C29 “Availability of BIM and sustainability databases” C28 “Technical support from software vendors” C27 “Standardization & simplicity of BIM and sustainability assessment software” 0.746 0.745 0.668 0.641 0.634 0.596 0.563 0.546 0.503 0.745 0.712 0.659 0.601 0.543 0.499 0.463 0.669 0.646 0.604 0.572 0.527 0.797 0.762 0.700 0.677 176 Percentage of variance explained Code Percentage Factor Eigenvalue of variance loading explained KDs for implementing smart sustainable practices Factor 5 – Information, risks & attitude-related drivers C19 “Number of subcontractors experienced with BIM projects” C20 “Client requirement and ownership” C17 “Increased involvement of project stakeholders in green projects” C18 “Clarity in requirements and measures for achieving sustainable projects” C14 “Shared risks, liability, and rewards among project stakeholders” 0.770 1.062 3.541 Cumulative percentage of variance explained 68.363 0.648 0.616 0.567 0.459 Source (of the KD’s items): Olawumi and Chan (2019d) 6.6 Discussion of survey findings This section will discuss the findings of this study in three aspects: (i) discussion of the clustered key drivers; (ii) discussion of the perspectives of the respondents on the KDs based on their regions; and (iii) the practical implications of the research findings. 6.6.1 Discussion of the clustered KDs The factor clusters representing the relationship among the underlying factors are designated with an identifiable and collective label (Sato, 2005), to aid its description (Olawumi & Chan, 2019a). The labels are based on the researcher’s perception, and hence are subjective (Chan & Hung, 2015). A metric known as factor scale rating was employed to rank the factor clusters in descending order of relevance (Chan, 2019; Chan & Hung, 2015). The factor scale rating (see Table 6.4) adds up the mean scores of each underlying factor of each cluster and divides the total mean score by the number of the underlying factor (Olawumi & Chan, 2019a). This section discusses the top-three factor clusters to conserve space and provided some recommendations to enhance smart and sustainable practices in construction projects. 177 Table 6.4: Ranking results of the factor scale rating for the KDs clusters Clustered Factor scale Factor label factor rating 1 Knowledge & industry-related drivers 3 Organizational & project-related drivers 2 Financial, legal & statutory drivers 4 5 6.6.1.1 Ranking 4.1456 1 4.01 2 3.9857 3 Technical drivers 3.98 4 Information, risks & attitude-related drivers 3.98 4 Knowledge & industry-related drivers Factor cluster 1 consists of nine underlying factors with a factor loading of more than 0.5 and has the highest factor scale rating of M=4.1456. The cluster focuses on issues related to the technical competence of staff, training scheme for specialists in smart sustainable practices, efficiency in the coordination of project stakeholders, firm’s awareness, and experience level among others. Gu and London (2010) and Ma et al. (2018) accentuated that staff of construction firms and government agencies required requisite training on both the technical and non-technical aspects of BIM to ease the implementation of smart sustainable practices in construction projects. Accordingly, they further argued that such training should be continuous because of the new roles and responsibilities emerging each day in the adoption of BIM and implementation of sustainability practices in the construction industry. Meanwhile, Antón and Díaz (2014) and Olawumi and Chan (2018b) emphasized the need for the development and availability of an in-house database to keep track of past and current projects’ data and its organization. The development of the database, as argued by Gu and London (2010), should align with the project management structure and organization of the firm, as well as suitable to meet the industry needs. However, such a database and its platform must be user-friendly and provide adequate data security. Abanda et al. (2015) advocated for the creation of action learning centers as a practical, knowledge-sharing, and problem-solving environment in which project stakeholders can share their experience, provide technical supports, and learn from each other. Also, an increased level in research and development (R&D) in the academics and industry 178 improved the level of adoption of smart sustainable practices in the construction industry (Aibinu & Venkatesh, 2014; Wong & Fan, 2013). 6.6.1.2 Organizational & project-related drivers The second most significant factor cluster is factor 3, which comprises of five key factors with a minimum factor loading of at least 0.5 and a factor scale rating of M= 4.01. The factor is concerned with project complexity in terms of its shape and system, client satisfaction level, the early involvement of project stakeholders, data compatibility and interoperability, and availability of affordable cloud-based technology. Ahn et al. (2014) reported that the current industry foundation class (IFC) schema used in the BIM system is inadequate for the integration of relevant information to aid building design simulation and energy modeling. Accordingly, to enhance the execution of smart sustainable practices in the built environment, more efforts need to be deployed by key project stakeholders in ensuring interoperability and data compatibility (Adamus, 2013; Ahn et al., 2014; Olawumi et al., 2018). Meanwhile, Hope and Alwan (2012) and Olawumi et al. (2017) reiterated the need for a clear understanding and evaluation of sustainability criteria in construction projects. there is a need to integrate BIM with sustainability assessment methods. Therefore, it is recommended for project stakeholders, organizations, professional bodies, and the various local authorities to work in sync to enhance the project and organization-related drivers towards improving the adopting of smart and sustainable practices in construction projects. 6.6.1.3 Financial, legal & statutory drivers Factor cluster 2 consisting of seven key underlying factors and a factor rating of M= 3.9857. The factor is related to the ease of securing funding for the acquisition of BIM software and its associated licenses, government support in the form of start-up funding for construction firms, development of an appropriate legal framework to guide its deployment in projects, the security of intellectual property and rights among others. Nanajkar and Gao (2014) acknowledged the hindrances posed by the high initial cost of procuring BIM software. Hence, to enhance the implementation of smart sustainable practices in construction projects, there must be a conscious effort and commitment by the relevant stakeholders to make the necessary funding available to aid the smooth implementation of smart and sustainable practices in 179 construction projects (Kivits & Furneaux, 2013; Olawumi & Chan, 2018d). Also, top management of construction firms should avoid being hesitant on making long-term future investments and commitment as regards the execution of BIM and sustainability practices in their projects (Gu & London, 2010; Hanna et al., 2013) towards making long-term impacts. The government should endeavor to support small and medium-scale construction firms with funding supports and incentives to aid their adoption of smart sustainable practices in-house and in their construction projects (Bin Zakaria et al., 2013; Olawumi & Chan, 2019d). 6.6.2 Comparative assessment of the KDs based on respondents’ regions It is imperative to examine the significance of the key drivers based on different regions or continents as these regions differ in the level of adoption and implementation of BIM (Jung & Lee, 2015; Olawumi et al., 2017) and sustainability (Olawumi & Chan, 2018a). Also, further analysis of the KDs for each region provides insights into the current state of the implementation of smart and sustainable practices and how the relevant stakeholders can team up to address the identified shortcomings. More so, the comparative assessment of the KDs per region will help avoid the problem of generalization of the research findings as well as provide the similarities. Table 6.5 shows the top-five significant drivers and bottom-three less significant drivers of implementing smart and sustainable practices for each region of the study’s respondents. These regions include Africa, Asia, Europe, and America in no particular order. Table 6.5: Comparative assessment of the KDs based on the respondents’ regions Africa Asia Europe America Overall Ranking Factors Mean Factors Mean Factors Mean Factors Mean Factors Mean C1 4.55 C1 4.17 C21 4.70 C1 4.83 C1 4.34 1 C21 4.50 C3 4.11 C9 4.45 C9 4.67 C3 4.27 2 C3 4.48 C2 4.09 C3 4.40 C21 4.58 C21 4.24 3 C9 4.45 C27 4.02 C26 4.40 C4 4.58 C2 4.22 4 C2 4.44 C15 4.02 C12 4.40 C2 4.58 C9 4.17 5 … … … … … … C30 4.08 C14 3.76 C6 3.70 C5 3.92 C6 3.87 28 C19 4.06 C13 3.69 C28 3.65 C30 3.75 C30 3.86 29 C6 3.92 C25 3.52 C7 3.45 C24 3.75 C25 3.79 30 180 The findings of the analysis for the key drivers for each region reveal some similarities. As shown in Table 6.5, the five most significant for all respondents are drivers C1, C3, C21, C2, and C9. Driver C1 which is concerned with the “technical competence of staff” and C2 – “greater awareness and experience level within the firm” is rated as one of the top-five significant drivers in Asia, Africa, and America regions but not in the European region. Also, factor C3- “training programs for crossfield specialists in BIM and sustainability” is ranked as a top-five driver in the built environment of Africa, Asia, and European regions; similarly, for drivers C9“effective collaboration and coordination among project participants” and C21- “early involvement of project teams” which are critical KDs for the European, African, and America construction sectors See Table 6.5). The results provided evidence that despite the significant progress made by some countries in Europe and America as regards the adoption of BIM and sustainability (Bernstein et al., 2012; Malleson, 2012; Olawumi & Chan, 2017) such as the United Kingdom, the United States, Canada among others (which are well represented among the study’s respondents) issues such as those represented by drivers C1, C2, C3, C9, and C21 are still salient in the construction sectors of these countries. Hence, to enhance the implementation of smart and sustainable practices, stakeholders in these regions must give considerable attention to these drivers. Also, in the Asia region, drivers C27 and C15, which is concerned with the standardization of BIM and sustainability assessment tools is regarded as an important factor in enhancing smart and sustainable practices in this region. Although, there have been some efforts in this regard, such as the development of BIM standards in Hong Kong (Chan et al., 2019a, 2019b), and development of green rating tools in Hong Kong (HKGBC, 2018), South Korea (IBEC, 2008), and Singapore (BCA, 2015). However, these standards are still insufficient to address some key issues of smart sustainable practices (Illankoon et al., 2017). Respondents from Europe highlighted drivers C26 and C12 as the salient drivers necessary for its adoption in this region. However, respondents from America and the African region gave less importance to driver C30 which implies the availability of open-source software will make little or no significant improvement to the implementation of smart and sustainable practices in these regions. Similarly, in the construction sectors of Europe and Africa, driver C6 is considered as less significant to the adoption of 181 smart and sustainable practices. Meanwhile in Asia, drivers C13 and C14 are given less consideration in these regions. The comparative evaluation of the perceptions of the respondents based on their regions as discussed in this section has shed more insight and perspectives on the trends and issues relating to the implementation of smart and sustainable practices in the construction industry of these regions. 6.6.3 Practical implementation of research findings The current study has identified the key drivers that can enhance the implementation of smart and sustainable practices in the construction industry. Also, the research has provided a purview of the significant KDs based on the different regions such as Europe, Asia, Africa, and America as well as based on the respondents’ professional and organization setups. The motivation behind the study and the findings of the study aligns with previous studies such as Ahvenniemi et al. (2017) and Allwinkle and Cruickshank (2011) who argued that in the considering buildings or cities as being smart; the evaluation should not be only on the use of smart tools in its design, construction, and operations but only the implementation of sustainability practices. These findings provided valuable contributions to theory and research as well as to industry practice. In curating the 30 key drivers for the implementation of smart and sustainable practices in the construction industry; the current study has provided an organized list of factors to aid the decision making of relevant stakeholders in the construction industry such as the government agencies, construction organizations, professional bodies, academics, etc. It is advised that more in-depth analysis can be done on these 30 KDs, as to how it can influence the adoption of smart and sustainable practices in each clime, construction projects, and firms. As discussed, these key drivers can form a basis for further discussion by the relevant construction stakeholders. The KDs and the findings based on the analysis of the different professions and organization setup, as well as regions of the respondents, can form part of a consultation instrument by relevant government agencies in charge of smart cities and sustainable development in designing localized policies and guidelines to aid the implementation of smart and sustainable practices. As revealed in the comparative 182 analysis of the significant KDs for each region; it is imperative for top management of construction firms and professional bodies to place more emphasis on the training of their staff as well as increasing their knowledge and awareness of BIM and sustainability practices through the organization of seminars, workshops, conferences, among others. The findings across the regions revealed the importance of collaboration and coordination among project stakeholders as well as their early involvement in construction projects. Hence, there is a need for the construction industry to avoid the use of traditional procurement methods and incorporate procurement methods and project management techniques that ensure the critical project stakeholders are involved in the early stages of the planning and design of such projects. However, despite the advantages and preeminence of open-source software development in other fields, the study respondents opined that it might not give construction stakeholders and firms the required leverage in the implementation of smart and sustainable practices in the built environment. 6.7 Conclusions The study investigated the concepts of smart and sustainable practices based on the extant literature towards identifying the key drivers (KDs) for the implementation of smart sustainable practices in the built environment. The different research approaches helped revealed the different implementation strategies, policies, and meaning of smart sustainable practices – as some countries focus more on the use of smart tools such as BIM, others on eco-issues, and some others tried to create a balance between the two concepts. The review of extant literature also revealed the deep-seated variance in the adoption, trend, and application of BIM and sustainability practices across the various regions, organization setups, and professional disciplines and noted the shortcomings of the existing green rating tools that place more consideration on the environmental sustainability. The key drivers of smart and sustainable practices in the construction industry have opined by the respondents included – the need to organize training programs and workshops for the training of cross-field specialists. Also, featured among the significant drivers is the technical competence of construction organizations’ staff; and early involvement and integration of key project personnel in the project. 183 A factor analysis of the key drivers yielded five-factor clusters. Therefore, based on the findings of the current study, the following recommendations and practical strategies are outlined for the relevant stakeholders in the construction industry towards enhancing the adoption of smart and sustainability practices in the built environment. These include: • There is a salient need for key project stakeholders, and government agencies to accord higher priority to the human capital development of their staff towards equipping and re-training them to meet up with the current trend of innovation in the industry. • Government agencies, as well as professional bodies, should provide synergy towards providing adequate and applicable subsidies or financial incentives to small and medium-scale construction firms to aid their adoption of smart sustainable practices in their construction projects. • Government regulatory agencies and professional bodies should work synchronously towards developing relevant policies and standards to aid the adoption of these concepts within the local context. • Construction firms should develop their in-house database platforms, which can help such firms in their implementation of smart sustainable practices as well as keep track of their projects’ data and information. • Top management of firms should prioritize the development and establishment of a good working strategy or model to implement smart sustainable practices. • Academic researchers and industrial practitioners are recommended to synergize their resources, experiences, and skills towards addressing some limitations found in existing smart sustainability tools and the structure of sustainability criteria, as well as providing technical support. • The development of open-source or affordable cloud-based technologies should be accelerated to mitigate against the potential barriers posed by the cost of purchasing the commercial desktop-based software. The study has examined the factors influencing the adoption of smart and sustainable practices based on the literature and the perceptions of the respondents, and it is revealed that the construction industry still lags in its adoption and implementation of smart and sustainable practices in the construction industry. The study has attempted to recommend the possible practical ways to overcome the 184 current deficiencies and determined the key drivers that could accelerate its implementation. Nevertheless, for these drivers and practical recommendations to achieve the preconceived goals, there must be synergy among all relevant construction stakeholders, firms, and government agencies towards achieving smart sustainable practices in the built environment. 6.8 Chapter Summary The construction industry has been evolving in recent years through the adoption of smart tools such as BIM to reduce the complexity in the construction process and optimize the project’s goals. This paper aims to identify and assess the key drivers for the implementation of smart sustainable practices in the construction industry. Inferential and descriptive statistical techniques were employed in analyzing the data collected via an international empirical questionnaire survey deployed in soliciting the perceptions of 220 construction professionals across 21 countries in this chapter. Factor analysis was used to categorize the identified key drivers into their underlying clusters for further discussion. Also, the data were analyzed based on the various groups and regions of the study’s respondents. The key drivers (KDs) are related to the technical competence of staff, as well as knowledge and awareness level within the industry; issues related to organizational and project’s strategy and policies; availability of financial resources, and development of relevant standards and policies to aid its execution among others. A comparative analysis of the perceptions of the different respondents’ groups was undertaken and discussed in this chapter. The analysis of the key drivers for the implementation of smart and sustainable practices in the construction industry is expected to aid the decision-making of the relevant stakeholders as well as serve as a consultation instrument for government agencies in their design of localized policies and guidelines to aid smart and sustainable urbanization. The findings revealed the gaps in the implementation of smart and sustainable practices in various climes and organization setups and provided useful and practical strategies for addressing the current hindrances during implementation. The chapter has generated valuable insights into the significant drivers that can enhance the implementation of smart and sustainable practices across regions. It is evident that synergy among the relevant stakeholders in the built environment will help accelerate the implementation of smart sustainable practices in the construction industry. More so, the study findings have provided profound 185 contributions to theory and research as well as to industry practice. The following chapter develops a sustainability assessment method for building projects within the context of the sub-Saharan region of Africa towards fulfilling Objective #2 of the study. 186 CHAPTER 7: DEVELOPMENT OF A BUILDING SUSTAINABILITY ASSESSMENT METHOD (BSAM) FOR DEVELOPING COUNTRIES IN SUB-SAHARAN AFRICA7 7.1 Chapter Overview The previous chapter analyzed the drivers/CSFs necessary to aid the implementation of BIM and sustainability practices – smart and sustainable practices – in building projects and the built environment. The current chapter based on the findings and deliverables of the previous chapters develops a sustainability assessment method for building projects within the context of the sub-Saharan region of Africa. The developed Building Sustainability Assessment Method (BSAM) scheme and its weighted criteria will be validated using two real-life building case studies. The proposed BSAM scheme will be compared with six widely used green rating systems such as LEED, BREEAM, etc. in this chapter. This chapter also provides an overview of the development of the BSAM scheme and its sustainability criteria. 7.2 Literature review This section discusses the various literature review and methodological processes, which informed the development of the BSAM scheme. 7.2.1 Establishing the key sustainability assessment criteria Extant literature and existing GBRS were reviewed as illustrated in Table 7.1 to shows the current trends and the relevant research gaps in these GBRS and the literature. The research gaps in these GBRS and the literature are enormous, however, only those identified in Table 7.1 are resolved in this study. The review of the literature was carried via a content analysis approach (see Downe-Wamboldt, 1992; White and Marsh, 2006) to identify the variables or criteria defined and considered by previous studies as important in the assessment of green buildings’ sustainability performance. This chapter is largely based on this published paper: Olawumi, T.O., Chan, D.W.M., Chan, A.P.C. & Wong, J.K.W. (2020). Development of a Building Sustainability Assessment Method (BSAM) for Developing Countries in sub-Saharan Africa. Journal of Cleaner Production, 263(August), Article 121514. https://doi.org/10.1016/j.jclepro.2020.121514 7 187 Table 7.1: Review of relevant sources for the development of the BSAM scheme S/N Sources Publication type Contributions to existing knowledge that supports the current study Research gaps 1. Review of extant literature a. Factors affecting green building Journal • Provided some barriers, benefits, projects (Ahmad et al., 2019; articles and drivers paradigms affecting descriptions of green building green buildings. paradigms. Cooper, 1999; Olawumi & Chan, 2019a) • Provided only conceptual • Provided recommendations for improving the implementation of green buildings. b. Review of some green rating Journal tools based on key articles • Provided in-depth reviews of the • Revealed that there are no development of some green rating suitable green rating tools for sustainability criteria (Ali & Al systems – credit points, the African continent. Nsairat, 2009; Alyami & methodology, data collection. This Rezgui, 2012; Humbert et al., provided insight into the 2007; Illankoon et al., 2017) development of the BSAM scheme. • Description of some key sustainability criteria. These criteria were modified for the development of the BSAM scheme. c. Development of green building Journal • Developed green building rating assessment methods. (Atanda, articles systems for some developing social and economic countries. sustainability. 2019; Banani et al., 2013; Gething & Bordass, 2006; • Utilize several aggregation Mahmoud et al., 2019) d. Implementation of green rating Article • • Little or no emphasis on the No related green rating techniques in the development of system suited for the local the assessment methods. context of countries in Africa. • Provided in-depth reviews of the • Revealed that there are no tools and review of its development of some green rating suitable green rating tools for practices. (AlWaer & Kirk, systems across North America, the African continent. 2012; Bunz et al., 2006; Chew Asia, and Europe. & Das, 2008; Kaur & Garg, • Discussed some key sustainability 2019; Sev, 2009) criteria and shows how the construction industry practices can lead to sustainable development. e. Issues with adopting the Journal existing green rating tools articles (Ding, 2008; Ding et al., 2018; Dwaikat & Ali, 2018; Olawumi & Chan, 2019d) • Revealed the salient challenges • Revealed the need to bridge hindering use of the existing green the current limitations in the rating tools. development of new green • Expatiate on the economic performance of green buildings. 188 rating tools. S/N Sources 2. Green rating systems a. LEED green rating system i. LEED (v. 4) for Homes Design and Construction (USGBC, 2017) ii. LEED v4 for Interior Design and Construction (USGBC, 2018c) iii. LEED v4 for Building Operations and Maintenance (USGBC, 2018b) iv. LEED v4.1 Operations and Maintenance (USGBC, 2018a) v. LEED v4 for Neigbourhood Development (USGBC, 2018d) b. BEAM Plus i. BEAM Plus New Buildings V2.0 (HKGBC, 2018) ii. BEAM Plus Existing Buildings Version 2.0 Comprehensive Scheme (HKGBC, 2016) Publication type Scheme documentation Contributions to existing knowledge that supports the current study • Features a three-level hierarchical Research gaps • structure of sustainability criteria social and economic sustainability. • Use of experts’ surveys to determine its credit points Little or no emphasis on the • No related scheme suited for the local context of countries • Different schemes for the various in Africa. building development stage • Greater emphasis on environmental sustainability such as IEQ and Energy, etc. • Relevant schemes for countries in North and South America, and Europe. Scheme documentation • Features a three-level hierarchical • structure of sustainability criteria • Involved the participation of No emphasis on the social and economic sustainability. • experts and industry practitioners Applicable for use solely in Hong Kong in developing the scheme. • Scheme available only for new and existing buildings. • Solely considers environmental sustainability. c. BREEAM i. BREEAM UK New Construction (BRE, 2018) ii. BREEAM In-Use International (BRE, 2016) Scheme documentation • Features a three-level hierarchical • structure of sustainability criteria social and economic • Use of experts’ surveys and consultation to determine its credit Little or no emphasis on the sustainability. • points No related scheme suited for the local context of countries in Africa. • Different schemes for the various building development stage • Greater emphasis on environmental sustainability such as IEQ and Energy, etc. • Relevant schemes for the UK and other countries in Europe. d. CASBEE for New Construction Scheme (IBEC, 2004, 2008) documentation • Features a two-throng assessment • category of quality and load • Divides the building project using hypothetical internal and external boundary. No emphasis on the social and economic sustainability. • Applicable for use solely in Japan • Sole emphasis on a few environmental sustainability 189 S/N Sources Publication type Contributions to existing knowledge that supports the current study Research gaps criteria. e. Green Mark i. Green Mark for Residential Buildings (BCA, 2015) ii. Green Mark Certification Standard for New Buildings (BCA, 2010) Scheme documentation • Features a three-level hierarchical • structure of sustainability criteria • Involvement of more than 130 and economic sustainability. • industry members and academics in the setting of metrics, No emphasis on the social Applicable for use solely in Singapore • Sole emphasis on a few assessment methods, and environmental sustainability performance levels criteria. • Different schemes for the various building development stage • Greater emphasis on environmental sustainability. f. IGBC Green New Buildings Scheme Rating System (IGBC, 2014) documentation • Features a three-level hierarchical • structure of sustainability criteria • Involvement of more than 1,923 No emphasis on the social and economic sustainability. • industry experts in its Applicable for use only in the Indian context. development. • Sole emphasis on environmental sustainability. 3. Green building technical notes a. Environmental design guide Technical note (CIBSE, 2007) • Provided some data values, • equations, and reference tables, Focused on environmental sustainability aspects. which were used in the evaluation of some environmental sustainability criteria for the BSAM scheme. b. GSA Lighting (GSA, 2019) Technical note • Provided some data tables which • Focused only on an were referenced in some IEQ environmental sustainability criterion in the development of the criterion. BSAM scheme. c. d. Energy and Use of Energy: Technical note • Provided some equations, and • Focused only on an Calculation and Application of reference tables which were used environmental sustainability OTTV and U-value (HKIA, in the evaluation of some energy criterion. 2012) criterion for the BSAM scheme. Green Mark i. Handbook on Energy Conservation in Buildings and Building Services (BCA, Technical note • Provided some data values, • Focused only on an equations, and reference tables, environmental sustainability which were used in the evaluation criterion. of some energy criterion for the 190 S/N Sources Publication type 1986) Contributions to existing knowledge that supports the current study Research gaps BSAM scheme. ii. Guidelines on Envelope Thermal Transfer Value for Buildings (BCA, 2004) More so, as shown in Table 7.1, a review of existing and leading green rating systems was conducted, such as BREEAM, LEED, BEAM Plus, CASBEE, Green Mark, Green Star, IGBC among others. These rating systems were sourced from their publicly available repository. The four steps of the review process highlighted above informed the identification of the three levels of the sustainability criteria. The levels of classification of the sustainability criteria of the BSAM scheme is based on the format of other wellestablished rating systems such as LEED, BREEAM, etc. which utilized similar system. Hence, the sustainability criteria consist of eight (8) key sustainability indicators – which are the level 1 criteria. For the sustainability attributes (level 2 criteria) – which are the subsets of their respective indicators, there are thirty-two (32) attributes; and lastly, for the sustainability sub-attributes – which are the subsets of their respective attributes, there are 136 sub-attributes. Most green rating systems whether designed for country-wide use (Ali & Al Nsairat, 2009; Ameen & Mourshed, 2019; Berardi, 2012; Escolar et al., 2019; Mahmoud et al., 2019) or regions (BCA, 2015; USGBC, 2018b, 2018a) only evaluate the existing building. However, the BSAM scheme proposed in this study is designed to assess the “greenness” and sustainability performance of both new and existing buildings, a similar model is used by the BREEAM system (BRE, 2016, 2018). Designing the BSAM scheme to cater for both new and existing buildings will ensure the sustainability potential of building projects in developing countries which can be forecasted in the early stages of the building project development. 7.2.2 Review of the selected existing green building rating systems This section provides an overview and justifications for the selection of six existing GBRS used for comparative assessment along with the proposed BSAM scheme. The primary criteria for selecting these GBRS is that they are developed by 191 members of the World Green Building Council (WGBC). According to the WGBC directory, there are 66 green building council members of three membership levels – established (38), emerging (10), and prospective (18) (WGBC, 2019). The “established” level members are defined as one with “a fully developed and operational organization that is running impactful green building programs of work” (WGBC, 2019). These green building councils are all independent, non-profit organizations with interest in the sustainability of the built environment and advance green building in their own countries. The six green rating tools selected for comparative evaluation in this paper are developed and implemented by established green building council members based on the WGBC classification. The second criteria in selecting these GBRS are identifying the number of building projects that have received green certification based on these rating tools. The six green rating systems and the number of certified green projects include BREEAM (>560,000) LEED (>90,000), Green Mark (>3000), IGBC (>1800), Green Star (>1500), and BEAM Plus (>467). Apart from these listed rating tools, CASBEE with over 14,000 certified green projects was excluded in the comparative analysis because this rating tool does not allocate credit points to each of its sustainability criteria but instead uses the Building Environment Efficiency score to rate projects (Illankoon et al., 2017). Although there are several green rating tools (Bernardi et al., 2017; Nguyen & Altan, 2011), but most of them are not members of the WGBC and are not widely used. Among these GBRS, three tools namely – BEAM Plus, IGBC, Green Mark are country-specific systems; while BREEAM, LEED, Green Star have been adopted and their criteria have been modified for more than one country (see Table 7.4). It is noted that BREEAM and LEED are the most widely used worldwide (Banani et al., 2013; Illankoon et al., 2017; Nguyen & Altan, 2011) and widely accepted. Hence, the six GBRS were reviewed in this paper and explained in Table 7.2. 192 Table 7.2: BSAM scheme and the six selected green building rating systems GB rating Year Region Countries systems LEED 1998 North America United States of America, Canada South America Europe Argentina, Brazil, Chile, Peru Asia mainland China, Korea Republic, India, Jordan, Chinese Taipei, United Arab Emirates Germany, Turkey, Spain, Poland, Sweden, Italy BEAM Plus 1996 Asia Hong Kong BREEAM 1990 Europe United Kingdom, Croatia, Germany, Netherlands, Poland, Spain, Sweden, Turkey, Norway, Switzerland, Austria, Luxembourg IGBC 2001 Asia Asia India Green Mark 2005 Asia Singapore Green Star 2003 Oceania Australia, New Zealand Africa South Africa Africa Target countries – sub-Saharan African countries BSAM 2019 scheme (current study) Source: The data was sourced from the respective websites of the six GBRS. Note: BEAM Plus – Building Environmental Assessment Method (Hong Kong); IGBC – Indian Green Building Council Rating (India); GB – Green Building More so, most of the six selected green rating systems have different schemes available for the certification of different buildings types. For instance, BREEAM has five main schemes available, namely (1) Communities (2) Infrastructure (3) New construction (4) In-Use (5) Refurbishment and Fit-out. 7.3 Research methods and data collection This section further discusses the research method and data collection approaches for the study. The methodological approach employed for the development of the proposed BSAM scheme as shown in Figure 7.1. Firstly, a comprehensive desktop review of relevant guidelines and technical notes on green building practices was undertaken, as well as a holistic review of the existing green building rating systems through peerreviewed journal articles and web pages using a content analysis approach (step A). These reviews formed the basis of the identification and establishing the key 193 sustainability criteria (indicators, attributes, and sub-attributes) of the BSAM scheme (step B). Using a similar approach adopted by the established green building councils for the development of existing green rating systems such as LEED, BREEAM, etc.; the current study utilized experts’ consultations and surveys to provide a quantitative measure suitable for the determination of credit points (score-weighting) and significance for the key sustainability criteria (step C). Hence, a set of questionnaire survey forms were distributed to the invited green building experts. The data collated from expert surveys and consultation help in establishing the ranking of the key BSAM scheme criteria based on the allocated credit points (steps B and C). More so, these preceding steps help to deduce the overall sustainability ranking of the ‘benchmark’ building project; and to establish the certification grade system of the BSAM scheme (step D and E). Lastly, towards justifying the contributions of the BSAM to address the particular context of the sub-Saharan region – it was validated using two building projects situated within the region (step F). Meanwhile, to show the precedence of the BSAM scheme over the existing green rating tools – a comparative assessment of selected green rating systems with the BSAM scheme was undertaken via comparing the score-weight distributions of the key criteria of the respective green building rating systems (step G). 194 Figure 7.1: Methodological approach to BSAM scheme development Note: Step A → Step B; there is a continuous loop between step C and step B until step B is established; step B → step D and step E. Step E → step F and step G. “→” = “leads to” 7.3.1 BSAM scheme: Its documentation and experts’ consultation The eight key sustainability indicators which are criteria to the sustainability performance of buildings and identified through the four stages of the review process include – ‘sustainable construction practices’, ‘site and ecology’, ‘energy’, ‘water’, ‘material and waste’, ‘transportation’, ‘indoor environmental quality’, and ‘building management’. As mentioned, these sustainability indicators consist of sub-sets called attributes as illustrated in Figure 7.2, which are evaluated in the determination of the sustainability performance of buildings. The attributes also contain sub-sets called sub-attributes – which are numerous (136) and open to future improvement. A complete structure and components of the proposed BSAM scheme are given in Appendix A. The sustainability indicator “sustainable construction practices” is only 195 assessed for new buildings and excluded in the sustainability assessment for existing building projects. Also, there are some subsets (sub-attributes) of the sustainability attributes which will not be evaluated for either new or existing buildings (Olawumi & Chan, 2019c). Readers can check the full documentation of the BSAM scheme for more details on which of the sub-attributes evaluate for new buildings and those that solely evaluate existing buildings. Figure 7.2: Key sustainability indicators (A-H) and their associated attributes For the evaluation of the sustainability criteria in building projects, it is recommended that the assessment be carried out by an independent third-party assessor. 7.3.2 Data collection and experts’ demographics Country-wide experts’ consultations were undertaken in seven major cities in Nigeria via a structured questionnaire surveys for six months which featured the engagement of 189 experts in the built environment towards the development of the BSAM scheme. In some instances, discussions and interviews were held with some 196 experts who require clarification on the identified sustainability criteria. It is worthy of note, that other well-established green building rating systems utilized surveys and interviews in developing the credit points for their rating systems. The participating experts were selected via a purposive sampling technique and snowball sampling. As shown in Table 7.3, the experts were requested to supply their personal details such as – their profession, years of experience in the construction industry, and whether the experts or their organization have been involved in making sustainability decisions (minor/major) in a building project. The questionnaire survey form is not the regular Likert scale-type survey form but provides spaces for the experts to input numerical values for the credit points, grading system levels, etc. Table 7.3: The experts' demographics Description Frequency Percentage (%) 35 31 25 42 7 18 17 7 4 3 189 18.5 16.4 13.2 22.2 3.7 9.5 9.0 3.7 2.1 1.6 100.0 38 53 33 40 25 189 20.1 28.0 17.5 21.2 13.2 100.0 Expert’s organizations involved in sustainability decisions? Yes 173 No 11 Not sure 5 Total 189 91.5 5.8 2.6 100.0 Major profession or occupation Architects Civil Engineers Project Managers Quantity Surveyors Structural Engineers Building Services Engineers Estate Surveyors Urban Planners Mechanical and Electrical Engineers Land Surveyors Total Years of working experience in the built environment < 5 years 5-10 years 11-15 years 16-20 years > 20 years Total The analysis of the invited experts revealed ten varied sets of key experts and stakeholders in the built environment who participated in the development of the BSAM scheme. Ali and Al Nsairat (2009) recommended that multi-stakeholders should be involved in developing green rating tools which was accomplished in this 197 research. More so, ninety-eight experts who represented more than 50% of the total number of participating experts have more than 11 years of working experience in the built environment. Also, it is worthy of note that more than 91% of the experts have been involved in making sustainability-related decisions in either current or previous building projects. Meanwhile, comparing the statistics of experts demographics of the current study with previous studies where authors have developed native green rating systems for a country – such as (i) Ali and Al Nsairat (2009) only employed four sets of stakeholders where invited namely academicians, project managers, field and design engineers; of which a total of 60 experts participated. (ii) For Mahmoud et al. (2019) only five sets of stakeholders participated namely civil engineers, mechanical and electrical engineers, sustainability experts, facility managers, and architects; of which 20 experts participated. (iii) Also, in Ahmad and Thaheem (2017), the study involved a higher proportion of its respondents from the academics, with a little percentage from the design and construction consultancy. In total 120 respondents participated in the study presented by Ahmad and Thaheem (2017). It can be concluded that the invited experts for this current study are well experienced (regarding their years of working experience and involvement in sustainability implementation in the built environment). Hence, this lend credence to the analyzed data. More so, the inputs of the key stakeholders in the built environment are well represented in the development of the BSAM scheme. The larger number of the participating experts for this study, when compared to the previous studies, validate the adequacy of the sample size. 7.3.3 Composition of the BSAM scheme - Repository The composition and full documentation for the BSAM scheme as developed in the course of this research is publicly available via a repository (Olawumi & Chan, 2019c) and as an e-supplement to this study (see Appendix A). The BSAM scheme is a 77-page documentation. 198 7.4 Results and discussion 7.4.1 BSAM Scheme: Analysis of its key sustainability assessment criteria and case study validation This section presents the analysis of the data collected via the multi-expert consultations. The results in this section while fulfilling one of the primary objectives of the study, also focus on the suitability of the BSAM scheme in practice within the built environment – especially in developing countries. Hence, this section includes the validation of the proposed BSAM scheme using a case study analysis involving two building projects – a residential building and a commercial building. 7.4.1.1 Key sustainability assessment criteria – score-weight determination and distribution The determination of the credit points (score-weights) for each of the sustainability sub-attributes was undertaken in consultation with industry experts in the built environment across seven states in Nigeria. The invited experts were asked to assign credit point scores to each of the sustainability sub-attributes. The invited construction industry experts were provided information as regards the importance of the credit point that is to be allocated to each sub-attribute and provided a guide based on the earlier four-stage review process. The ratio of the mean average of the credit scores of the sustainability criteria to the nearest unit is presented in Appendix D1, and Figure 7.3 shows the score-weight distribution for each sustainability indicator (A – H) in terms of the weightings of their sustainability attributes. The summation of the credit points of the respective sub-attributes gives its total credit point for its attribute. Equation (i) is used to establish the score-weight (credit point) of each indicator and it is based on the mean score metric which divides a set of values by the number of values in that set. The mean score metric was also employed in other well-established rating systems such as LEED, BREEAM, etc. The set of values are the numerical values inputted by the invited experts within the spaces provided in front of each sustainability criteria. 𝑊𝑧 = 𝑊𝑒𝑖𝑔ℎ𝑡 (𝑐𝑟𝑒𝑑𝑖𝑡 𝑝𝑜𝑖𝑛𝑡) = ∑ 𝐶𝑃𝑎 𝑁 − − − 𝑒𝑞𝑛 (𝑖) Where ∑ 𝐶𝑃𝑎 = summation of the credit points of the attributes of sustainability indicator (z) 199 𝑁 = the number of attributes for the sustainability indicator (z) 𝑊𝑧 = score-weight [credit points (CP)] of the sustainability indicator (z) For example, the 𝑊𝑧 (F) for sustainability indicator F is 5.333 CP (see Appendix D1) which is a resultant of the average of its three attributes (F1= 7; F2= 7; F3= 2 credit points). Appendix D1 reveals that sustainability indicators “sustainability construction practices,” “energy,” “indoor environmental quality,” and “building management” (A=11.5 CP; C= 10.67 CP; G= 7.0 CP; and H= 6.5 CP, respectively) are rated critical to the sustainability performance of the project based on the score-weight (𝑊𝑧 ) of the attributes. The score-weights of the sustainability criteria are vital to establishing the ranking of each sustainability assessment indicator and the overall sustainability rating of buildings. 7.4.1.2 Determination of the significance of the sustainability sub- attributes This section discusses and presents the results of the importance of each sustainability sub-attribute based on their classification as either “required,” “optional,” or “negligible.” The classification of the sub-attributes is basically for the comparison or ranking of building projects; and not to exclude the assessment of the sustainability sub-attribute when evaluating a building project’s sustainability performance, especially if the sub-attribute is marked “optional.” However, if a subattribute is marked “negligible,” it will be excluded in project ranking or comparison. The industry experts invited to participate in the development of the BSAM were also asked to classify the 136 sustainability sub-attributes and rate the level of significance of the sub-attributes in respect of each sustainability attributes using a three-point scale (“required,” “optional,” and “negligible”). The “classification by percentage score” adopted by Olatunji et al. (2017) was modified for use in this study. The classifications are as follows: (1) if ≥65% of the experts rated the sub-attribute as “required” or less than 40% of the experts rated the sub-attribute as “optional,” it was classified as a required sub-attribute. (2) if less than 65% of the experts rated the sub-attribute as “required” and between 40 and 65% of the experts rated it as “optional”, the sub-attribute was classified as optional; 200 and (3) if less than 50% of the experts ranked the sub-attribute as required, and less than 40% rated it optional, the sub-attribute was classified as negligible. Based on these classification criteria, Appendix D2 presents the classification of the significance of each sustainability sub-attribute; seventy-three (73) sub-attributes were classified as required, while 63 sub-attributes were classified as an optional attribute. No sub-attribute was classified as negligible. Column “inference” in Appendix D2 shows the resultant significance of each sub-attribute based on the classification criteria. Figure 7.3: Score-weight distribution for each of the key sustainability assessment criteria 7.4.1.3 Establishing the ratio of each sustainability indicator As shown in Figure 7.4, sustainability indicators A, C, and G were regarded by the experts as the sustainability criteria that should be given the highest priority in the evaluation of a building sustainability performance compared to the other five criteria. 201 Further, the percentage contribution (ratio, 𝑆𝑟 ) of each sustainability indicator (A – H) to the overall sustainability performance of a building project is evaluated using equation (ii) and as shown in Figure 7.4. The overall sustainability status (𝑆𝑝 ) of the building project is deduced by calculating the score-weights between the ∑ 𝑊 of the benchmark and the proposed case using equation (iii). The benchmark case – is a building project demonstrating the optimum sustainability performance – that has achieved the maximum score-weight (credit point) for the sustainability criteria. The proposed case – is the building project under observation or being assessed. 𝑆𝑟 = 𝑊𝑧 𝑋 100 − − − 𝑒𝑞𝑛 (𝑖𝑖) ∑ 𝑊𝑧 Where ∑ 𝑊𝑧 = summation of the score-weights of the sustainability indicator (z) 𝑊𝑧 = score-weight [credit point] of the sustainability indicator (z) 𝑆𝑟 = Percentage contribution of each sustainability indicator Figure 7.4: Percentage contribution of each of the key sustainability assessment criteria Note: A – ‘Sustainable construction practices’; B – ‘Site & Ecology’; C- ‘Energy’; D – ‘Water’; E – ‘Material & waste’; F – ‘Transportation’; G – ‘Indoor environmental quality’ (IEQ); H – ‘Building management’. 𝑆𝑝 = ∑ 𝑊𝑠 𝑋 100 − − − 𝑒𝑞𝑛 (𝑖𝑖𝑖) ∑ 𝑊𝑖 202 Where ∑ 𝑊𝑠 = summation of the score-weights of the sustainability indicator (proposed case) ∑ 𝑊𝑖 = summation of the score-weights of the sustainability indicator (benchmark case) 𝑆𝑝 = The overall sustainability performance (in percentage, %) 7.4.1.4 The BSAM certification grading system The grading system for the BSAM scheme was derived by soliciting from the invited experts to input two numerical values (minimum and maximum thresholds) in the questionnaire survey form for the six identified performance levels (outstanding, excellent, very good, good, acceptable, and unclassified) of the BSAM scheme. The six performance levels have been earlier identified by the authors based on a similar approach adopted by the other green rating systems such as LEED, Green Star, BEAM Plus, BREEAM, etc. The experts were asked to provide the two numerical values (thresholds) which must be within the range of 0 and 100. The mean value of these thresholds for the individual performance level was calculated as shown in Figure 7.5. Figure 7.5: BSAM Six-grade certification system 203 Hence, to calculate the certification grade level for a green building, the result of the evaluation of equation (iii) which assesses the overall sustainability performance (𝑆𝑝 ) of the building project is used. The BSAM certification grade system is based on the performance level on which the resultant 𝑆𝑝 , falls. The BSAM certification grade system is a scale from 0 to 100, which represents the six performance levels. Therefore, based on the thresholds of the six performance levels of the BSAM scheme (see Figure 7.5), a green building project must have a 𝑆𝑝 value of 40 (“acceptable” grade) before it can be green certified under the proposed BSAM scheme. The highest performance level for the BSAM tool is the “outstanding” grade level, which is from 82 – 100%. 7.4.1.5 Case study validation for the BSAM scheme Two case study of projects were used to validate the suitability, adequacy, reliability, and appropriateness of the BSAM scheme in practice within the built environment – especially in developing countries of sub-Saharan Africa. These include a residential building and a commercial building. These two case studies share similar tropical climate classification with varying rainy and dry seasons. The first case study is a residential building (a duplex) project (Project A) located within the south-eastern part of Nigeria. It is classified as a “new building” based on BRE (2018) classification as the building is still less than one year of occupancy. The second case study is a commercial building project (Project B) located within the south-western part of Nigeria – which featured three laboratories and other offices at the ground floor and include two meeting halls, a conference room among other offices at the first floor – and can also be classified as a “new building”. The two case studies (projects A and B) were assessed using the BSAM scheme documentation (Olawumi and Chan, 2019b) and sustainability criteria weights, and the results are shown in Appendix D3 and Table 7.4. The result revealed in Appendix D3 shows the weighting average at the sustainability attributes and indicators levels (Table 7.4) because results at these levels help understand where the building projects perform well and where it is inadequate. However, it must be noted that the two case study projects were assessed based on their score-weights at the subattributes level – which is the building block of the BSAM scheme. A radar diagram 204 shown in Figure 7.6 maps the standing of the case study building projects (projects A & B) in terms of their sustainability indicators’ weightings (𝑊𝑠 ). The analysis of the weightings (𝑊𝑠 ) for the sustainability indicators for the case studies – projects A and B (Table 7.4) reveals that project A outperforms project B in three sustainability criteria. These are criteria B, E, and F with weighting values of 2.67, 4.88, and 4.67 respectively. Also, project B outperforms project A in five sustainability criteria which are A, C, D, G, and H with weighing values of 8.42, 7.33, 4.17, 5.92, and 4.75 respectively. Hence, to improve the projects’ sustainability performance of projects; the clients, designers, and other key stakeholders need to critically assess the projects’ credit points (score-weights) of the sustainability attributes level of the individual projects as shown in Appendix D3. This will help to evaluate where the building is performing well and where there is a need to improve the overall sustainability performance. The overall sustainability performance (𝑆𝑝 ) of the case study projects is also presented in Table 7.4. Project A has a 62.6% overall sustainability performance when its scoreweights (∑ 𝑊) is normalized with the benchmark case, while project B has a better overall sustainability performance status of 69.11%. Based on the BSAM scheme grade certification system, project A can be classified as a “good” rated green building and project B as a “very good” rated green building. 205 Table 7.4: Weighting average for the sustainability indicators and the overall sustainability performance (Sp) values for the case study projects Sustainability Indicators Maximum Weight (W) [Benchmark case] ∑CPi Wi Sr (%) Project A ∑CPs Ws Project B Sr (%) ∑CPs Ws Sr (%) A 69 11.50 19.91 37.5 6.25 17.28 50.5 8.42 20.89 B 15 5.00 8.66 8 2.67 7.37 7.5 2.50 6.20 C 32 10.67 18.47 20 6.67 18.43 22 7.33 18.20 D 18 6.00 10.39 10.5 3.50 9.68 12.5 4.17 10.34 E 23 5.75 9.96 19.5 4.88 13.48 17.5 4.38 10.86 F 16 5.33 9.24 14 4.67 12.90 8.5 2.83 7.03 G 42 7.00 12.12 28.75 4.79 13.25 35.5 5.92 14.68 H 26 6.50 11.26 11 2.75 7.60 19 4.75 11.79 57.75 100.00 36.17 100.00 40.29 100.00 Total (∑) 𝑺𝒑 100% 62.63% 69.77% Note ∑CPi,s = total of each sustainability indicator’ attribute weights; 𝑆𝑝 = The overall sustainability performance (in percentage, %); 𝑊𝑖,𝑠 = score-weight of the indicator; 𝑆𝑟 = Percentage contribution of each indicator. A – ‘Sustainable construction practices’; B – ‘Site & Ecology’; C- ‘Energy’; D – ‘Water’; E – ‘Material & waste’; F – ‘Transportation’; G – ‘Indoor environmental quality’ (IEQ); H – ‘Building management’. Figure 7.6: Comparison of the score-weight distribution of the two case study projects (A & B) in terms of their sustainability indicators The results and analysis presented in this section offer key stakeholders and decision-makers a convenient and efficient means and methods for assessing the 206 sustainability performance of a building project. The breakdown of the analysis into the sustainability attributes and indicators levels also help in understanding how the green building project functions at each sustainability assessment criteria level and assist in pinpointing where it fails to perform adequately. 7.4.2 Comparison of the proposed BSAM scheme with the six selected existing green building rating systems This section compares the BSAM scheme with the six well established GBRS. This paper focuses on the ‘new construction’ schemes and latest versions of the six selected green rating tools for uniformity purpose (Illankoon et al., 2017). The different sustainability criteria and the scheme of the six selected GBRS used in this study are identified in Table 7.5. 7.4.2.1 Allocating credit points to the key sustainability assessment criteria The proposed BSAM scheme and the six selected GBRS identified in the previous section have different sustainability criteria (Table 7.5). Specific sustainability criteria are identical in some of the rating tools such as ‘energy’ and “IEQ”; IEQ is addressed in BREEAM and BEAM Plus as “health and wellbeing” (Table 7.5). Sustainable sites (or land use), materials, and waste are another set of sustainability criteria addressed directly in most of the green rating tools (BRE, 2018; GBCA, 2017; HKGBC, 2018; IGBC, 2014; USGBC, 2017); except in Green Mark where ‘waste’ and ‘materials’ are addressed under the “resource stewardship” criterion while the sustainable sites are termed ‘tropicality’ under the “climatic responsive design” criteria of the Green Mark (BCA, 2015). Also, all the selected GBRS has the ‘innovation’ criterion embedded as a key criterion or as a sub-level of other criteria; and it is intended to reward innovative techniques employed in the projects. In BEAM Plus, the ‘innovation and additions’ criterion is addressed as a bonus criterion (BRE, 2018). 207 Table 7.5: Distribution of the credit points for key sustainability assessment criteria of BSAM scheme and the six selected green building rating tools LEED v4. (Design & Construction - 101 credit points) Integrative process (2) Location and transportation (15) Sustainable sites (7) Water efficiency (12) Energy and atmosphere (29) Indoor environmental quality (16) Innovation (5) BEAM Plus v2.0 (New Building - 133 credit points) Integrated design and construction management (25) Sustainable sites (20) BREEAM 2018 (New Construction - 149 credit points) IGBC v3.0 (New Buildings - 100 credit points) Management (21) Sustainable architecture and design (5) Health and wellbeing (20) Materials and waste (14) Energy use (31) Water use (12) Energy (31) Site selection and planning (14) Water conservation (18) Health and wellbeing (21) Materials (14) Regional Priority (4) Green Mark v5.0 (Residential building - 140 credit points) Climatic responsive design (30) Building energy performance (30) Resource stewardship (30) Smart and healthy building (30) Advanced green efforts (20) Green Star v1.2 (Design & As-built - 100 credit points) Management (14) Indoor environmental quality (17) Energy (22) Water (12) Materials (14) Land use and ecology (6) Emissions (5) Innovations (10) Transport (12) Water (9) Waste (11) Land use and ecology (13) Pollution (12) Innovation (10) BSAM v1.0 (New Buildings 241 credit points) Energy efficiency (28) Building materials and resources (16) Indoor environmental quality (12) Innovation and development (7) Sustainable construction practices (69) Site and ecology (15) Energy (32) Water (18) Material and waste (23) Transportation (16) Indoor environmental quality (42) Building management (26) More so, the seven green building rating tools have differing sustainability criteria (see Table 7.5) and to provide a common basis to compare these rating tools – this study adopts the eight key sustainability criteria of the BSAM scheme to allow uniformity in the comparative assessment of the seven rating tools. Furthermore, in the review of the selected rating tools, it was observed that for instance – in Green Mark®, some sub-levels of the sustainability criteria such as ‘sustainable construction practices,’ ‘transportation,’ ‘site and ecology,’ and ‘water’ identified in BSAM was evaluated under the ‘climatic responsive design’ criterion in Green Mark rating tool (BCA, 2015). In LEED®, ‘non-toxic pest control’ was identified in ‘sustainable sites’ criterion but was attributed under ‘IEQ’ criterion in the BSAM scheme as the credit point helps to 208 provide a better IEQ; also, ‘regional priority’ criterion in LEED (USGBC, 2017) was attributed under ‘site and ecology’ in the BSAM scheme as it includes credit points that have an impact on site and designs. Similar re-arrangement of the credit points of the sustainability criteria of the selected green rating tools was undertaken to conform with the structure of the eight BSAM scheme sustainability criteria. As a result, the credit points of the criteria for the six selected green rating tools were separately attributed based on the BSAM criteria (Table 7.6). Based on these normalized credit points, radar diagrams (Figure 7.7) and a comparison chart (Figure 7.8) were developed to further compare the key sustainability criteria and the seven green rating tools. 7.4.2.2 Similarities in the radar diagrams for the green building rating tools in comparison to the BSAM scheme As illustrated in Figure 7.7, LEED and BEAM Plus have a similar pattern in the structure of their diagram based on the credit point allocation among the key sustainability criteria except for the ‘sustainable construction practices’ criterion which was considered in a greater context in BEAM Plus. Also, the pattern of the BREEAM and Green Star radar diagrams is quite similar except for the ‘sustainable construction practices’ criterion, which was considered in a little more detail in BREEAM. These findings are akin to the normative literature (Fowler & Rauch, 2006; Illankoon et al., 2017) which reported that Green Star was developed based on the BREEAM scheme. The BEAM Plus which was also developed based on the BREEAM system also share similar pattern except for the less evaluation of the ‘building management’ in the former. 209 Table 7.6: Allocation of the credit points for the eight key sustainability assessment criteria (A H) for each of the green rating tools Sustainability A B C D E F G H Total criteria LEED CP 2 14 17 15 11 7 30 5 101 BEAM-Plus BREEAM IGBC Green Mark Green Star BSAM Scheme (current study) % 1.98 16.83 14.85 10.89 6.93 29.70 4.95 100.00 18 13.8 6 9 CP 31 13 15 4 27 6 123 % 14.63 7.32 25.20 10.57 12.20 3.25 21.95 4.88 100.00 CP 16 11 27 14 20 12 35 18 153 % 10.46 7.19 17.65 9.15 13.07 7.84 22.88 11.76 100.00 CP 3 14 27 16 20 3 13 4 100 % 3.00 27.00 16.00 20.00 3.00 13.00 4.00 100.00 CP 8 14.0 0 12 55 8 22 0 33 2 140 % 5.71 8.57 39.29 5.71 15.71 0.00 23.57 1.43 100.00 CP 4 8 12 15 16 10 23 12 100 % 4.00 8.00 12.00 15.00 16.00 10.00 23.00 12.00 100.00 CP 69 15 32 18 23 16 42 26 241 % 28.63 6.22 13.28 7.47 9.54 6.64 17.43 10.79 100.00 Note: A – ‘Sustainable construction practices’; B – ‘Site & Ecology’; C- ‘Energy’; D – ‘Water’; E – ‘Material & waste’; F – ‘Transportation’; G – ‘Indoor environmental quality’ (IEQ); H – ‘Building management’. CP – Credit points; % - the percentage of each criterion of the total score of the scheme. Also, the pattern of the IGBC and BREEAM system radar diagrams is similar except for the better consideration for the ‘building management’ and ‘sustainable construction practices’ criteria in BREEAM. The pattern of the BSAM scheme, however, is somewhat similar to most of the other selected green rating tools (except Green Mark), though the massive consideration of the ‘sustainable construction practices’ criterion in the BSAM scheme is an exception. The ‘sustainable construction practices’ criterion is a massive improvement on the existing GBRS to suit the local context of the developing countries with the sub-Saharan region of Africa. 7.4.2.3 Differences in the radar diagrams for the green building rating tools in comparison with the BSAM scheme An evaluation of the comparison of the selected green building rating tools shows that the ‘IEQ’ criterion has the highest consideration in most of the rating tools except 210 for BEAM Plus and the proposed BSAM scheme where it receives a little lesser attention (Figures 7.7 and 7.8). The normalized credit points for the ‘IEQ’ for the rating tools range from 13% to 29.7%. The ‘IEQ’ criterion is given the highest priority in LEED, with about 29.7% of the total credit points, followed closely by BREEAM (22.88%). In the BSAM scheme, the ‘IEQ’ criterion is given the second priority behind the ‘sustainable construction practices’ criterion. Illankoon et al. (2017) reported that there is an increased concern about occupant satisfaction in buildings that IEQ denotes due to the prevalence of ‘sick building’ syndrome. However, a survey by El Asmar et al. (2014) reveals a weak link between the intended performance of the building as regards IEQ at the design stage and its actual performance during occupancy. Berardi (2012) identified IEQ as an essential criterion in the assessment of green buildings. Figure 7.7: Radar diagrams for the green building rating tools based on the credit points of their key sustainability assessment criteria 211 Next in line is the ‘energy’ criterion, which is given second priority, except in BEAM Plus, IGBC, and the Green Mark, where it receives the highest consideration. Also, the normalized credit points for the ‘IEQ’ for the rating tools range from 12% to 39.29% of the total score. The ‘energy’ criterion receives 39.29% of the overall credit point in Green Mark when compared to the 16.83% in LEED. A review of the Green Mark rating system shows that the ‘energy’ criterion is distributed across three out of the five credit criteria in Green Mark® with each criteria receiving a very high credit allocation (BCA, 2015). This finding corresponds with one of the main objectives of Green Mark to achieve “increased energy effectiveness”; which explains the higher percentage weighting of the ‘energy’ criterion in Green Mark compared to the other rating tools (BCA, 2015). The ‘energy’ criterion is rated as a third priority within the BSAM scheme after the ‘sustainable construction practices’ and ‘IEQ’ criteria. Berardi (2012) and Kamaruzzaman et al. (2016) reported that the ‘IEQ’ and ‘Energy’ criteria are the salient sustainability criteria in all green building rating tools which correlate with the findings reported in this paper. Figure 7.8: Comparison chart for the green building rating tools based on the credit points of their key sustainability assessment criteria 212 Note: A – ‘Sustainable construction practices’; B – ‘Site & Ecology’; C- ‘Energy’; D – ‘Water’; E – ‘Material & waste’; F – ‘Transportation’; G – ‘Indoor environmental quality’ (IEQ); H – ‘Building management’. Nevertheless, an analysis of 490 buildings from the green building council database by Berardi (2012) revealed that the ‘energy’ criterion is the most difficult to achieve. In line with one of the main objectives of Green Mark to “increased energy effectiveness”; which explains its higher percentage weighting compared to the other rating tools. Kamaruzzaman et al. (2016) reported that the ‘IEQ’ and ‘Energy’ criteria are the salient sustainability criteria in all green building rating tools which correlate with the findings reported in this paper. The ‘material and waste’ criterion also receives some consideration by all the rating tools with its normalized credit points ranging from 9.54% to 20% of the total score. The ‘material and waste’ criterion received its highest priority in the IGBC scheme, followed closely by the Green Star. Zhang et al. (2017) stressed that there is a need for an increased focus on the recycling of building materials and waste to promote a sustainable material performance, as well as encourage the use of local materials. The Australian Bureau of Statistics (2012) also reported that the building construction works contribute about 26% of total waste in the ecosystem; hence, it is important to evaluate this criterion in buildings to ensure an optimal sustainable performance. Each of the rating tools gives the ‘water’ criterion some focus on the radar diagram except for the Green Mark system, where it receives the lowest credit point of 5.71% of the total score. It gets the highest score in the IGBC scheme with a normalized credit point of 16%. Berardi (2012) revealed that the ‘water’ criterion has the highest percentage of fulfillment in most certified green buildings. Meanwhile, among the selected green building rating system, only the BSAM scheme considers ‘water conservation’ as a priority criterion. Cheng et al. (2016), in an analysis of buildings in Taiwan, reported that green buildings achieved 60% water savings than non-green buildings. Alwisy et al. (2018) stressed that the use of waterefficient equipment could help buildings achieve significant reductions in water usage. Also, per Tam et al. (2019a) who stated that the use of sustainable water facilities rather than the conventional ones can help improve water efficiency. The ‘material and waste’ criterion also receive some consideration by all the green rating tools with its normalized credit points ranging from 9.54% to 20% of the total score. The ‘material and waste’ criterion received its highest priority in the IGBC 213 scheme, followed closely by the Green Star. For the transportation’ criterion, the Green Mark scheme has given no priority or credit point to the ‘transportation’ criterion, and it also gave less than 2% of its total score to the ‘building management’ criterion. 7.4.2.4 Precedence of the BSAM scheme over the existing green building rating tools This section highlights and discusses certain key sustainability criteria that are not identified in the six GBRS, but which were identified in the extant literature. It is noteworthy that these key sustainability criteria were considered in developing the BSAM scheme. Also, the BSAM scheme embeds virtually all the criteria in the six selected green rating tools, but the inclusion of these key criteria in the BSAM scheme is based on their importance within the local context of developing countries in sub-Saharan Africa. Olawumi and Chan (2018a), Olawumi and Chan (2019c) and ISO 15392 reported that for a building project to be regarded as a green building it needs to fulfill the triple-bottom pillar of sustainability – that is, must be environmentally, socially, and economically sustainable. However, with reference to the findings of the review of the six selected green rating tools and as reported in the extant literature (see Ali & Al Nsairat, 2009; Illankoon et al., 2017; Sev, 2009); these existing green building rating tools considers only the factors pertaining to the minimization of the environmental impacts of buildings while ignoring the key social and economic criteria in the evaluation of buildings. Illankoon et al. (2017) and Gibberd (2005) further stressed the need for the future development of green rating tools especially in developing countries to address this shortcoming in the existing GBRS. Shari (2011) identified some key social sustainability criteria such as education and awareness, local people and employment, and inclusiveness of opportunities which were not considered by the selected green rating tools. Also, Liu et al. (2013) highlighted ‘stakeholder relation’ as another social criterion that should be considered by green rating tools, but which are not currently included in the existing rating tools. All these social sustainability criteria were given significant consideration in the newly developed BSAM scheme under the ‘sustainable construction practices’ criterion. Social sustainability criteria identified under the BSAM scheme include 214 ‘engagement of local firms’, ‘local employment opportunities’, ‘public participation’, ‘compliance with social standards’, ‘education and skills development’ among many other key social sustainability attributes. Berardi (2012) argued further that the neglect of social sustainability criteria in existing rating systems makes these GBRS incomplete as it contradicts a key pillar of the sustainable development dimension. For the economic sustainability criteria, the extant literature (Ali & Al Nsairat, 2009; Liu et al., 2013; Wei et al., 2011) has discussed extensively the need for an increase in the consideration of economic criteria in the development of building projects which are currently lacking in the existing green building rating systems. The proposed BSAM scheme considered economic criteria such as ‘enhanced local economy,’ ‘reuse of construction materials’ among others. Zhang et al. (2017) reiterated the need to link the economic and environmental criteria of green buildings to allow for harmony in its assessment. Another key sustainability indicator (cultural aspect) identified in the literature (Banani et al., 2013; Salehudin et al., 2012; Shari, 2011) is the ‘protection of cultural heritage’ which is not provided in most green rating tools except in BEAM Plus. The criterion is catered for in the proposed BSAM scheme as ‘integration of cultural heritage in design.’ More so, the ‘management’ criterion is given less consideration in the existing green rating tools; even though this sustainability criterion has been much discussed in the literature (Illankoon et al., 2017; Sev, 2009). In the BSAM scheme, the ‘management’ criterion is given due consideration to about 11% of the total credit point, which gave the criterion the fourth priority among the eight key sustainability criteria identified in BSAM. Also, as regards the ‘materials and waste’ criterion, none of the existing green rating tools consider this key criterion for assessment at the construction stage of the green building development. Another key sustainability criteria unavailable in the existing GBRS but given consideration in the proposed BSAM scheme are the ‘safety and health’ and ‘ethics and equity’ criteria. Also, as regards the ‘materials’ criterion, the existing GBRS focus on the material type (category) while the BSAM scheme focuses both on the former as well as whether the materials are locally sourced. Meanwhile, Ali and Al Nsairat (2009) reported that unlike developing countries are typically conscious of the economic and social pillars of sustainable development 215 than the environmental construct. Hence, this study addressed the imbalance by developing a holistic GBRS towards achieving sustainable development goals. 7.6 Conclusions Green building rating systems provide a means to create and monitor the development of sustainable buildings and infrastructure. The relevance of the development of the proposed BSAM scheme lies in addressing the shortcomings of the existing green building rating systems and providing a holistic green rating tool suitable to the local context of developing countries in sub-Saharan Africa. The research established the key sustainability criteria of the proposed BSAM scheme based on a four-step review process discussed in the study’s methodological approach. A review of the extant literature using the content analysis approach identified the need for green building rating systems to focus on the three pillars of sustainability. The eight key criteria were identified for inclusion in the BSAM scheme. The full documentation of the BSAM scheme documentation is provided as a supplementary data and multi-expert consultations helped in determining the credit weighting of each of the BSAM sustainability criteria. The sustainability assessment criteria weights and the significance of each sustainability sub-attributes were also established based on the analysis of the data collected via the experts’ consultations. The criteria-based ranking of the BSAM scheme is generated by aggregating the credit points of its sustainability attributes and sub-attributes. Also, the percentage of the total score-weights for each sustainability criterion and the certification grading system scales – outstanding, excellent, very good, good, acceptable, and unclassified, which are measured on the scale of 0-100% was established. Forty percent is the minimum threshold before a building can receive green certification under the BSAM scheme. Two case studies of building projects (residential and commercial buildings) were employed to validate the suitability, practicality, and appropriateness of the BSAM scheme in practice within the built environment. Furthermore, to validate and demonstrate the improvement of the proposed BSAM scheme over the existing green building rating systems, a comparative analysis of the BSAM scheme with six selected common green rating tools – LEED, BEAM Plus, BREEAM, IGBC, Green Mark, and Green Star – was carried out in this study. An 216 analysis of the existing green rating tools reveals a different set of sustainability criteria and to allow for uniformity of comparison of the green rating tools in this paper; the credit points of these rating tools were re-assigned based on the eight key sustainability criteria of the BSAM scheme. Based on the comparison of these green rating tools, the following conclusions were derived – (1) The existing green building rating tools place more emphasis on the environmental aspect of sustainability and overlooked the social and economic parameters; while the BSAM scheme gave a steady consideration to the three aspects of sustainability; thereby providing a better holistic evaluation of green buildings. (2) The BSAM scheme embeds virtually all the key sustainability criteria required for the assessment of green buildings based on the local context, while some green rating tools fail to cater for some of the key sustainability criteria adequately. More so, (3) There are some similarities in the credit points allocation among the green rating tools, as shown in the patterns of their radar diagrams while there are differences. (4) The BSAM scheme, BREEAM, and Green Star shows a more balanced consideration in the allocation of credit points for the key sustainability criteria. (5) The ‘management’ and ‘sustainable construction practices’ criteria were given higher priority in the BSAM scheme when compared to the other selected green rating tools; although, these criteria are of vital to the sustainability performance of buildings. (6) All the green building rating tools place more significant consideration to the ‘IEQ’ and ‘energy’ criteria, although the ‘energy’ criterion was found to be the most difficult to achieve while the ‘water’ criterion is the easiest to achieve. As evidenced by the findings in this paper, the BSAM scheme encompasses the necessary key sustainability criteria as well as an improvement of the existing green rating tools. Limitations to the proposed BSAM scheme, includes that the scheme like the other green rating tools, fails to address the complex relationships among the key sustainability criteria. Also, another limitation of the study is the use of aggregation of points which limits the expressions of the key sustainability criteria. These two shortcomings are addressed in chapter 8. In summary, the following are the significant contributions of the study. (1) The proposed BSAM scheme includes effective guidelines towards evaluating green buildings as well as the documentary evidences to be assessed and verified to 217 ascertain the fulfillment of the key sustainability criteria. (2) It also covers the maintenance and improvement of the sustainability performance of the buildings throughout their lifecycles. (3) Implementing the proposed BSAM scheme can promote greener buildings and sustainable urban development and guide the design team as well as the construction team to employ greener technologies. (4) It also fulfills the need for a technical scheme through the experience-based ranking of the key sustainability criteria. It is recommended for each developing country in the sub-Saharan region to establish their own green building councils towards joining the global body. More so, countries using the existing green rating systems such as LEED, BREEAM, BEAM Plus, etc. which emphasizes the environmental sustainability are implored to examine the social and economic sustainability criteria in the BSAM scheme updating their respective GBRS. Also, stakeholders in the built environment are encouraged to adopt and test the proposed BSAM scheme in evaluating their building projects to accelerate the implementation of this green rating tool. Future research can focus on expanding the scope of the key sustainability criteria and adding more variables at each sub-level – attributes and sub-attributes. Conclusively, the study has contributed to the existing body of knowledge by developing the BSAM scheme for developing countries as a step towards establishing a universal working green building rating system for future use. 7.7 Chapter Summary The consideration of the regional context in the development of green building rating systems is well established in the previous literature, and this informs the development of a sustainability assessment method for sub-Saharan Africa. Hence, a multi-expert consultation method was carried out in Nigeria – which is the largest economy in the region. This was performed via a structured questionnaire survey and interview approaches to identify the key sustainability assessment criteria, assign score-weights to the various criteria, and establish the certification grading system of buildings. The developed Building Sustainability Assessment Method (BSAM) scheme and its weighted criteria were validated using two real-life building case studies. The established BSAM scheme was compared to six widely used green rating systems. The comparative analysis reveals that the score-weights and 218 priorities of the BSAM scheme were remarkably different from the existing rating systems. The study findings also show the increasing focus on the indoor environmental quality and energy criteria by all the rating systems. The developed BSAM scheme, meanwhile, has adequately considered the three main pillars of sustainable development unlike the existing green rating tools. Hence, it is expected for the proposed BSAM scheme to promote greener buildings and enhance sustainable urban development in the region. This chapter provided an overview of the development of the BSAM scheme and its sustainability criteria. The following chapter attempts to use a more robust weighting methodology to address the two limitations of the conventional “aggregation of points” method adopted in this chapter. 219 CHAPTER 8: APPLICATION OF GENERALIZED CHOQUET FUZZY INTEGRAL METHOD IN THE SUSTAINABILITY RATING OF GREEN BUILDINGS BASED ON THE BSAM SCHEME8 8.1 Chapter Overview The previous chapter developed a sustainability assessment method for building projects within the context of the sub-Saharan region of Africa using the conventional “aggregation of points” approach. The current chapter addresses this limitation and others by employing a MCDM technique – the generalized Choquet fuzzy integral (GCFI) method – to determine the weights of the sustainability criteria and develop the sustainability evaluation index of the BSAM scheme, while solidifying the development of the BSAM scheme. The developed sustainability rating model (BSAM scheme) will then be validated in four real-world building case studies to demonstrate its usefulness and robustness in practice. More so, the significance of the GCFI technique over other weighting methodology and MCDM tools will be discussed. 8.2 Introduction Optimum determination of the sustainability performance or greenness of buildings and infrastructure is vital to fulfilling the objectives of sustainable development in the built environment. Assessment of the impact of the building throughout its lifecycle on the built environment involved an intricate process which includes a hierarchical structure of several variables that comprises the three pillars of sustainability – social, environmental, and economic sustainability (Mahmoud et al., 2019; Olawumi & Chan, 2018a). These variables or sustainability criteria (as referred to henceforth in this study) need to be controlled or regulated to achieve the intended level of sustainability performance and reduce their harmful impacts on the building users and the environment. Building and infrastructure projects are essential and contribute to societal wellbeing, economic development, and the safeguard of the environment. However, the design This chapter is largely based on this published paper: Olawumi, T.O. & Chan, D.W.M. (2020a). Application of Generalized Choquet Fuzzy Integral Method in the Sustainability Rating of Green Buildings based on the BSAM Scheme. Sustainable Cities and Society, 61, Article 102147. https://doi.org/10.1016/j.scs.2020.102147 8 220 of these structures, their locations as well as the use of resources (material and energy), waste and emissions generation have a significant effect on the sustainability of the built environment. Therefore, to reverse this negative trend and ensure the prudent allocation and use of resources throughout the building lifecycle, it is essential to develop methods of assessing the impacts arising from the project as well as the building users’ activities. Moreover, it is necessary to evaluate the efficacy of the various policies, plans, and strategies for the building project and ascertain the extent they influence the sustainability performance of the building and the overall sustainable development. Several studies have been conducted on assessing the sustainability performance of buildings and sustainability rating tools such as the Leadership in Energy and Environmental Design (LEED), Building Research Establishment Environmental Assessment Method (BREEAM), BEAM Plus has been developed. However, these studies and green rating tools fail to present a unified sustainability evaluation criteria or variables, that is, only catered for 1-2 of the three sustainability pillars. Apart from these, these studies utilized aggregation of points that have many shortcomings which have been discussed in the extant literature (see Ahmad & Thaheem, 2018; Illankoon et al., 2017; Mahmoud et al., 2019). Two key shortcomings of this “aggregation of points” approach is that it does not allow for interactivity the main criteria and sub-criteria as well as does not reflect interdependence of these criteria (Kurt, 2014; Ozdemir & Ozdemir, 2018); For instance, Ahmad and Thaheem (2018) developed an economic sustainability assessment framework for residential buildings and study using normalization methods; although the study focused solely on economic sustainability criteria, it still left out some key criteria such as reuse of construction materials, local economy, etc. Also, the normalization methods adopted are inadequate. Similarly, Atanda (2019) employed the Analytic Hierarchy Process (AHP) to identify the social indicators for green buildings assessment. However, the study fails to consider the environmental and social construct of sustainable development. The widely used green building rating systems such as LEED, BREEAM, BEAM Plus, Green Mark, etc. (BCA, 2015; BRE, 2018; HKGBC, 2018; USGBC, 2017) mainly focused on the environmental sustainability with little or no consideration for the other aspects of sustainability. More so, these green rating tools employed 221 simple addition of points that are incapable of expressing the interactions among the sustainability criteria. Ali and Al Nsairat (2009) developed a green assessment tool for Jordan with a little more focus on the environmental and social constructs but utilized the AHP methodology, which is less effective in dealing with sustainability variables of hierarchical nature (Krishnan et al., 2015). Mahmoud et al. (2019) utilized one of the Multi-Criteria Decision Making (MCDM) techniques – fuzzy TOPSIS to develop a green assessment tool for existing buildings for Canada and Egypt. However, the developed tool, like the other assessment frameworks in the existing literature, focused solely on environmental sustainability. Although the fuzzy TOPIS methodology aims to estimate the gaps between the expected and perceived sustainability performance in the study, it still fails to consider the interaction among the decision criteria. However, it is difficult to ignore the interactions among the sustainability criteria in a hierarchical structure. For instance, the “thermal performance of building envelope” consider in this study under the energy criterion have significant effect on the “indoor air quality” and “visual comfort” recognized under the indoor environmental quality (IEQ) criterion. Tan and Chen (2010), Krishnan et al. (2015), and Perçin (2019) confirmed that in real scenarios these criteria hold some degree of relationships that requires a robust weighting tool such as the GCFI. As emphasized by Perçin (2019), additive models such as the MCDM techniques such as AHP, TOPSIS, etc. and their fuzzy versions, as well as other existing methodologies, are insufficient to evaluate dependent and non-additive sustainability criteria as expressed in this study. Krishnan et al. (2015) present some examples to illustrate the interactive characteristics of criteria and the significance of using additive and non-additive (such as GCFI) operators. Given the above gaps in the literature as regards the (i) the need for a unified sustainability assessment system that encompasses the social, economic, and environmental criteria; (ii) previous studies utilized additive MCDM technique which is insufficient to assess dependent and subjective criteria; and (iii) the need to capture the interrelationships among these sustainability assessment criteria. The current study aims to utilize an MCDM technique – the generalized Choquet fuzzy integral (GCFI) method for the evaluation of the sustainability rating of green buildings based on the decision criteria of the Building Sustainability Assessment Method (BSAM) scheme green rating system as well as addressed the above 222 literature gaps. The BSAM scheme is a green building rating system that took adequate and equal consideration for the three pillars of sustainability (Olawumi & Chan, 2019c). The BSAM scheme was developed in the course of this research project, and the current study describes the development of the weighting methodology for the BSAM scheme using the GCFI algorithm. The GCFI method according to extant literature (Bebčáková et al., 2011; Çakır, 2017; Zhang et al., 2019) adequately addressed the issue of the interactions among dependent sustainability criteria in a hierarchical structure. More so, according to Perçin (2019), using independent criteria in solving MCDM problems as evident in extant literature regardless of their effect on each other will limit its ability to evaluate the subject matter adequately. 8.3 State of the art: The General Choquet Fuzzy Integral (GCFI) Method The Choquet fuzzy integral has found its usefulness in solving numerous MCDM problems in the extant literature. The GCFI technique has discussed in Section 8.2 and later in this section is superior to the other MCDM techniques. More so, it has only been applied once to solve an MCDM problem relating to sustainability issues when Ozdemir and Ozdemir (2018) employed the GCFI approach to select the best alternative among five residential heating systems. In the industrial sector, Demirel et al. (2010) utilized GCFI to resolve a warehouse logistic issues for a large Turkish company while in the hospitality sector, Perçin (2019) used it to evaluate the quality of hospitals’ websites; and Karczmarek et al. (2018) employed GCFI for face recognition and classification; also, GCFI was used to evaluate equipment maintenance quality (Zhang et al., 2019), hybrid image encryption (Hosseinzadeh et al., 2019), and supplier selection for a steel factory (Çakır, 2017). Other applications of GCFI to solve MCDM problems include voice recognition, traffic surveillance, temperature prediction (Fang et al., 2010), game theory, neural networks (Qin et al., 2016), among others. The GCFI is regarded as a better alternative to the fuzzy ANP (Demirel et al., 2010). 8.3.1 Historical development of the GCFI method The first development of the GCFI methodology was the Choquet integral introduced by Sugeno (1974) as a flexible aggregator operator and the generalization of the Lebesque integral (Demirel et al., 2010; Grabisch & Roubens, 2000). It involves 223 generalizing the “weighted average method,” the “Ordered Weighted Average” (OVA) operator, and the max-min operator (Grabisch et al., 2000). It is a nonadditive measure and aims at representing the significance of a criterion and the interactions among dependent criteria (Demirel et al., 2010; Perçin, 2019). More so, to define these fuzzy integrals, a set of values are required for the criterion, and these values are in fuzzy measures; also, if these criteria have sub-sets (as seen in this study) – the values of importance should be defined as well. The next phase of the development of GCFI was the presentation of the generalized form of the Choquet integral by Auephanwiriyakul et al. (2002); and further improvement through the use of linguistic expressions and fusion of information among criteria, as well as the use of interval measurements by Tsai and Lu (2006). This helps to overcome the ambiguity of the questionnaire scale terms. Unlike other MCDM techniques, the GCFI adequately cater to the dependence between the decision-makers judgments and the assessed criteria (Perçin, 2019); and this makes it differ significantly from the Sugeno integral. The Sugeno and Choquet integral operators can deal with interactive decision criteria however the GCFI is better suited across many research areas (Narukawa & Torra, 2007). Furthermore, the Choquet integral is ideal for numerical and quantitative problems where cardinal aggregation is required while the Sugeno integral is best suited for qualitative problems where only the ordinal aggregation of the attributes is essential (Krishnan et al., 2015). The GCFI is a type of fuzzy set operation which depends heavily on information aggregation at hierarchical levels towards making informed decisions (Chiang, 1999), and its ability to model interactions of the decision criteria set it apart from others. 8.3.2 Underlying conditions for adopting the GCFI method In the context of MCDM analysis, Krishnan et al. (2015) defined aggregation as the process of evaluating the weights of a set of decision criteria under evaluation into a global score; and based on this single final score, the alternatives (e.g., building projects) can be classified or ranked. Hence, before employing GCFI, the decisionmakers must input the importance value of the decision criteria and their subsets. An aggregation operator must have two fundamental properties, which are the monotonicity and boundary conditions (Cheng & Hsu, 1991; Karczmarek et al., 224 2018). However, the GCFI has an additional property for its fuzzy measures (λmeasure), which makes the Choquet integral more robust due to the ease of usage and good “degree of freedom” of the λ-measure (Krishnan et al., 2015). The λ-measure of the GCFI technique represents the “degree of additivity” the criteria hold. Hence, according to Gürbüz et al. (2012) and Hu and Chen (2010): (i) If λ < 0, it implies that the decision criteria share sub-additive (redundancy) effect. It means an increase in the overall sustainability performance of a building can be achieved by enhancing the sets of criteria which have higher weights or individual importance. (ii) If λ > 0, it implies that the decision criteria share a super-additive (synergy) effect. It means to achieve an increase in the overall sustainability of a building, all the sets of criteria must be enhanced regardless of their weights or individual importance. (iii) If λ = 0, it indicates that the sets of decision criteria have non-interactive characteristics. Therefore, for the GCFI method to be employed for any MCDM problem, especially for a hierarchical network of decision criteria, the fuzzy measure, λ must either be λ < 0 or λ > 0. A limitation of the fuzzy measure, λ is that it requires a large quantity of information from decision-makers (Krishnan et al., 2015). Hence, as discussed in section 8.4.1.1, the current study utilized a sizeable number of decision-makers (experts) to determine the overall building sustainability evaluation index. Zhang et al. (2019) added that the λ-fuzzy measure has significant advantages over the other four fuzzy measures in the extant literature, and it has a relatively simple structure. Yildiz and Yayla (2017) and Qin et al. (2016) demonstrated that the classical MCDM techniques are ineffective in solving real-world decision problems, unlike the fuzzy MCDM methods which are more suitable to cope with uncertainty issues in practical applications. 8.3.3 Advantages of the GCFI methods over other MCDM techniques In a comparison of the GCFI with some other widely used MCDM techniques; unlike the GCFI, the AHP rely on independent decision criteria (Zhang et al., 2019) and does not adequately capture qualitative criteria (Çakır, 2017) which makes it unsuitable for resolving non-additive and dependent models. More so, Çakır (2017), in analyzing an MCDM problem for a steel-producing company carried out a comparative assessment of the different MCDM techniques such as fuzzy TOPSIS, 225 fuzzy ANP, fuzzy DEMATEL, EAM and found the GCFI method to be superior. Also, fuzzy TOPSIS can handle hierarchical problems but not interactive criteria (Kurt, 2014; Ozdemir & Ozdemir, 2018). Moreover, other advantages of the GCFI over the other weighting methodology include: i. It allows for the interactivity among the main criteria and its sub-criteria (Kurt, 2014; Ozdemir & Ozdemir, 2018). ii. Its use of trapezoidal fuzzy numbers and range computations using integral provides a better result (Kurt, 2014; Ozdemir & Ozdemir, 2018). iii. Its use of signed fuzzy measure allows for an efficient approach to information aggregation (Fang et al., 2010); and iv. The usefulness of the GCFI algorithm in many information fusion and data mining problems (Yang et al., 2005). All these make the GCFI method a more suitable and practical weighting method than the other MCDM techniques. The steps for the GCFI algorithm (Demirel et al., 2010; Grabisch & Roubens, 2000; Kurt, 2014; Ozdemir & Ozdemir, 2018; Perçin, 2019) are summarized in section 8.4. Meanwhile, to minimize round-off error and ease the speed of running the GCFI algorithm in this study as illustrated in section 8.4, a PHP-based cloud platform was developed to record the input collated from each invited experts for each criteria and their sub-sets, analyzed the data based on the GCFI algorithm, and to output the solutions as presented in subsequent sections. 8.4 Application of the GCFI Method: Sustainability Rating of Green Buildings This section discusses how an MCDM method in the form of the generalized Choquet fuzzy integral algorithm was applied to develop a sustainability evaluation index that can be employed to rate the sustainability performance of green buildings. See Figure 8.1 for the research methodology framework employed in this study. Further in this section, the developed building sustainability evaluation index (BSEI) is used to evaluate four real-life case studies of building projects. The set of equations, that is Eq. (1) to Eq. (13), except Eq. (2) as discussed in Section 8.4 are based on the General Choquet Fuzzy Integral (GCFI) methodology 226 as adopted from the extant literature (see Dong et al., 2016; Huang et al., 2010a; Kurt, 2014; Ozdemir & Ozdemir, 2018). Figure 8.1: Overall framework of the research methodology 8.4.1 Determination of the Building Sustainability Evaluation Index (BSEI) In this study, the GCFI algorithm was applied to the developed Building Sustainability Assessment Method (BSAM) scheme (see Appendix A) – a green building rating system specifically designed for countries in the sub-Saharan region of Africa 227 (Olawumi & Chan, 2019c). The structure of the BSAM scheme framework is illustrated in Figure 8.2. The BSAM scheme has three sustainability criteria levels in its hierarchical structure: which are sustainability indicators (SI), attributes (SA), and sub-attributes (SSA) which have 8, 32, and 136 criteria respectively. These criteria contain both quantitative and qualitative information. The GCFI algorithm was employed to determine the weightings of these sustainability criteria (SI, SA, & SSA) towards establishing the overall BSEI which can then be used to (a) rate the sustainability performance of a building; and (b) select the best green building alternative or rank a set of building projects – based on their different key sustainability criteria. Figure 8.2: Hierarchical structure of the sustainability evaluation criteria of the BSAM scheme 228 8.4.1.1 Experts’ demographics Bebčáková et al. (2011) reported that the weights of the sustainability criteria must be estimated expertly. Hence, the input data for this study is based on the responses derived from the decision-making group, which was composed of 189 experts in the built experts over six months. The experts were selected using purposive and snowball sampling techniques. See Figure 8.3 for the analysis of the demographics of the experts. As shown in Figure 8.3, the invited experts are from ten distinct and varied professions; and this multi-expert consultation approach was recommended by Ali and Al Nsairat (2009) who pointed out that a diverse set of key participants should be involved in the process of developing green building assessment rating tools. Previous studies that adopt the GCFI method, such as Kurt (2014), who used the GCFI method and fuzzy TOPSIS to select the best site for a nuclear power plant, utilized three power system experts. Also, Ozdemir and Ozdemir (2018), who applied the GCFI to prioritize five residential heating systems sought the opinion of three experts to provide the importance values of the four heating system criteria. A comparative assessment of the statistics of the experts involved in this study and the existing literature (where the GCFI algorithm was adopted) shows that an increased number of participating experts in the decision-making. It also revealed the involvement of a highly experienced set of experts in the subject matter based on their years of working experience and participation in the implementation of sustainability practices in the built environment. Thus, this lends further credence to the input data for the development of the BSEI and its subsequent application to rate the sustainability performance of four case studies building projects. 229 Figure 8.3: The experts’ demographics 8.4.1.2 The fuzzification process As mentioned, the experts are invited to identify the degree of significance of each sustainability criteria that are critical to the sustainability performance of buildings. The sustainability criteria provide the right mix of key criteria that satisfies the three pillars of sustainable development as it relates to building projects – social, economic, and environmental sustainability. The experts were requested to provide five sets of four numbers (trapezoidal fuzzy numbers) to express the five-linguistics variables – “very high,” “high,” “medium,” “low,” and “very low” (see Appendix C1). That is, each set of linguistics variables corresponds to four numbers, which represent a trapezoidal fuzzy number that comprises a minimum threshold (lowest number in the fuzzy set), two median thresholds, and the maximum threshold (highest number in the fuzzy set). 230 Step 1: Fuzzifying the ‘degree of importance’ levels Given the sustainability criteria, 𝑖; the linguistics terms of the experts for the “degree of importance,” the “degree of significance” of each sustainability criteria, and the tolerance zone can be quantified. Table 8.1 shows the relationship between the “trapezoidal fuzzy numbers” (TFN) and degree of importance (linguistics variables) on a five-linguistic-term scale. The TFN has shown in Table 8.1 and, as represented in Figure 8.4, is the average value based on the mean of the input values for the ‘degree of importance’ provided by the 189 invited experts based on Eq. (1) (Ozdemir & Ozdemir, 2018). 𝑃̃𝑖 , 𝐴̃𝑖 = ̃𝑡 ∑𝑘 𝑡=1 𝐴𝑖 𝑡 ∑𝑘 𝑡 ∑𝑘 𝑡 𝑡 ∑𝑘 ∑𝑘 ̃ 𝑖2 ̃ 𝑖3 ̃ 𝑖4 ̃ 𝑖1 𝑡=1 𝑎 𝑡=1 𝑎 𝑡=1 𝑎 𝑡=1 𝑎 𝑘 = ( is parameterized 𝑘 , 𝑘 , 𝑘 , 𝑘 ) (1) Where 𝑘 is the number of invited experts; expert 𝑡, and the linguistic terms for the “degree of importance” by 𝑡 𝑡 𝑡 𝑡 𝐴̃𝑖 = (𝑎̃𝑖1 , 𝑎̃𝑖2 , 𝑎̃𝑖3 , 𝑎̃𝑖4 ), where 𝑡 𝑡 𝑡 𝑡 𝑎̃𝑖1 , 𝑎̃𝑖2 , 𝑎̃𝑖3 , 𝑎̃𝑖4 are trapezoidal fuzzy numbers of 𝐴̃𝑖 ; and the ‘degree of significance’ is 𝑡 𝑡 𝑡 𝑡 𝑡 𝑡 𝑡 𝑡 parameterized by 𝑃̃𝑖 = (𝑝̃𝑖1 , 𝑝̃𝑖2 , 𝑝̃𝑖3 , 𝑝̃𝑖4 ), where 𝑝̃𝑖1 , 𝑝̃𝑖2 , 𝑝̃𝑖3 , 𝑝̃𝑖4 are trapezoidal fuzzy numbers of 𝑃̃𝑖 . Figure 8.4: Representation of the Trapezoidal Fuzzy Numbers 231 Table 8.1: Relationship between the TFN and degree of importance (linguistics variables) on a five-linguistic-term scale Degree of importance Trapezoidal fuzzy numbers Label Linguistic terms VH Very High (0.774, 0.834, 0.860, 0.876) H High (0.649, 0.704, 0.744, 0.816) M Medium (0.556, 0.589, 0.631, 0.676) L Low (0.411, 0.471, 0.500, 0.574) VL Very Low (0.252, 0.282, 0.324, 0.416) Step 2: Determination of the Tolerance Zones and perceived ‘degree of significance’ for the key sustainability criteria More so, considering the relationship between the perceived degree of significance and the tolerance zone of each key sustainability criteria, the TFN was used to quantify all the linguistic terms inputted by the experts (see Table 8.2). Two ‘degree of significance’ TFN numerical values were derived. Firstly, the average ‘degree of significance’ value (𝑃̃𝑖 ) for each sustainability criterion based on the inputs of the experts (see Appendix C2) using the five-linguistic-term scale was calculated using Eq. (1) and as presented in Table 8.2. Secondly, the average ‘degree of significance’ value (best alternative) (𝑇̃𝑖 ) was calculated as follows. (i) The experts were asked to rank the sustainability subattributes for each criterion on a three-scale point (Required, Optional, Negligible), otherwise known as the RON scale (see Appendix C3). (ii) The ‘Required’ scale (R) was given a value of 1.0; ‘Optional’ scale (O) – a value of 0.5; and ‘Negligible’ scale (N) – a value of 0.0. (iii) The mean RON values (𝑅̃𝑖 ) for each criterion (attributes) is calculated by Eq. (2): 𝑅̃𝑖 = ∑𝑘𝑡=1 𝑅̃𝑖𝑡 1 × 𝑘 𝑛 (2) Where 𝑘 is the number of invited experts; expert 𝑡, number of SSA within each criterion (SA) 𝑛, and the RON values (see Table 8.2) for each criterion is represented 232 by 𝑅̃𝑖 . (iv) The individual ‘degree of significance’ (best alternative) (𝑇̃𝑖 ) for each key criterion (A1 – H4) was calculated by multiplying the mean RON values (𝑅̃𝑖 , Eq. (2)) for each criterion (SA) with the highest numerical values of the highest linguistic-term scale (that is, VH). For instance, in Table 8.2 – the 𝑅̃𝑖 of A1 is 0.667 and VH is (0.774, 0.834, 0.860, 0.876); hence, the ‘degree of significance’ value (best alternative) (𝑇̃𝑖 ) is (0.516, 0.556, 0.574, 0.584). The determination of the average ‘degree of significance’ (best alternative) (𝑇̃𝑖 ) allows for the development of the BSEI and evaluating the sustainability performance of different sets of building as later seen in this study without having to repeat the entire GCFI algorithm for each new set of buildings. A similar approach was adopted by Mahmoud et al. (2019) in using fuzzy TOPSIS to develop an index. Hence, determining the average ‘degree of significance’ (best alternative) (𝑇̃𝑖 ) in this study offers an improvement on existing GCFI algorithm as employed in previous studies. The minimum (min) tolerance and the maximum (max) tolerance value is based on the lowest and highest linguistic-term scale of the sustainability criteria. Meanwhile, the tolerance zones in Table 8.2 are obtained by combining the first two numerical values of the ‘min tolerance value’ with the last two numerical values of the ‘max tolerance value’ for each sustainability criteria. For instance, tolerance zone [M, VH] for sustainability criteria A1; the numerical values of M and VH based on Table 8.1 is (0.556, 0.589, 0.631, 0.676) and (0.774, 0.834, 0.860, 0.876) respectively. Hence, the combined tolerance zone (𝑒̅𝑖∝ ) for criteria A1 is (0.556, 0.589 ,0.860, 0.876). 233 Table 8.2: Average values of the degree of significance and tolerance zones of each criterion (SA) using TFN Average ‘degree of significance’ value (Best Alternative) (𝑇̃𝑖 ) Average ‘degree of significance’ value for each criterion (𝑃̃𝑖 ) Min. Tolerance Value Max. Tolerance Value Tolerance Zone (𝑒̅𝑖∝ ) RON values (𝑅̃𝑖 ) A1- Project Site and Design (0.660, 0.709, 0.745, 0.789) M VH (0.556, 0.589, 0.860, 0.876) 0.667 (0.516, 0.556, 0.574, 0.584) A2- Societal Engagement (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.547 (0.423, 0.456, 0.471, 0.479) A3- Safety & Health (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.650 (0.503, 0.542, 0.559, 0.570) A4- Ethics & Equity (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.708 (0.548, 0.591, 0.609, 0.621) A5- Construction Material & Waste (0.660, 0.709, 0.745, 0.789) M VH (0.556, 0.589, 0.860, 0.876) 0.638 (0.493, 0.532, 0.549, 0.559) A6- Project Management (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.657 (0.509, 0.548, 0.566, 0.576) Sustainability Criteria (Attributes) A- “Sustainable Construction Practices” B- Site and Ecology B1- Site Selection (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.583 (0.452, 0.486, 0.502, 0.511) B2- Site Management (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.563 (0.435, 0.469, 0.484, 0.493) B3- Reduction of Heat Island Effect (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.550 (0.426, 0.459, 0.473, 0.482) C1- Energy Performance (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.540 (0.418, 0.450, 0.464, 0.473) C2- Energy Management (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.600 (0.464, 0.500, 0.516, 0.526) C3- Energy Efficient Systems & Equipment (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.571 (0.442, 0.477, 0.492, 0.501) D1- Water Efficiency (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.500 (0.387, 0.417, 0.430, 0.438) D2- Water Management (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.563 (0.435, 0.469, 0.484, 0.493) D3- Water Efficient Systems & Equipment (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.333 (0.258, 0.278, 0.287, 0.292) E1- Sustainable Purchasing Practice (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.589 (0.456, 0.491, 0.507, 0.516) E2- Efficient Use & Selection of Materials (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.583 (0.452, 0.486, 0.502, 0.511) E3- Waste Management Practice E4- Ease of Conversion of Building Functions F- Transportation (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.560 (0.434, 0.467, 0.482, 0.491) (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.500 (0.387, 0.417, 0.430, 0.438) F1- Alternative Means of Transport (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.583 (0.452, 0.486, 0.502, 0.512) F2- Community Accessibility (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.500 (0.387, 0.417, 0.430, 0.438) C- Energy D- Water E- Material and Waste 234 Average ‘degree of significance’ value for each criterion (𝑃̃𝑖 ) Min. Tolerance Value Max. Tolerance Value Tolerance Zone (0.528, 0.576, 0.612, 0.672) VL G1- Visual Comfort (0.597, 0.650, 0.684, 0.736) G2- Indoor Air Quality (0.597, 0.650, 0.684, 0.736) G3- Thermal Comfort Average ‘degree of significance’ value (Best Alternative) (𝑇̃𝑖 ) VH (𝑒̅𝑖∝ ) (0.252, 0.282, 0.860, 0.876) RON values (𝑅̃𝑖 ) 0.625 (0.484, 0.521, 0.538, 0.548) L VH (0.411, 0.471, 0.860, 0.876) 0.578 (0.447, 0.482, 0.497, 0.506) L VH (0.411, 0.471, 0.860, 0.876) 0.583 (0.452, 0.486, 0.502, 0.511) (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.500 (0.387, 0.417, 0.430, 0.438) G4- Acoustic Performance (0.559, 0.609, 0.662, 0.700) VL VH (0.252, 0.282, 0.860, 0.876) 0.500 (0.387, 0.417, 0.430, 0.438) G5- Hygiene (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.578 (0.447, 0.482, 0.497, 0.506) G6- Building Amenities (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.563 (0.435, 0.469, 0.484, 0.493) H1- Operation & Maintenance (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.592 (0.458, 0.493, 0.509, 0.519) H2- Security (0.597, 0.650, 0.684, 0.736) L VH (0.411, 0.471, 0.860, 0.876) 0.500 (0.387, 0.417, 0.430, 0.438) H3- Risk Management (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.547 (0.423, 0.456, 0.471, 0.479) H4- Green Innovations (0.528, 0.576, 0.612, 0.672) VL VH (0.252, 0.282, 0.860, 0.876) 0.500 (0.387, 0.417, 0.430, 0.438) Sustainability Criteria (Attributes) F3- Transport Management G- Indoor Environmental Quality H- Building Management 235 + Step 3: Evaluation of the Fuzzy Valence Functions [𝒇− 𝒊,∝ , 𝒇𝒊,∝ ] for the key sustainability criteria – at the SA and SI levels Using 𝑓̃𝑖 ∈ 𝐹̃ (𝑆) as the fuzzy valence function (Kurt, 2014; Ozdemir & Ozdemir, 2018), the significance of the criterion for the ‘best alternative (BA)’ building prototype can be normalized using Eq. (3). 𝑓̃ = ‖ ∝∈[0,1]𝑓𝑖̅∝ = ‖ + − [𝑓𝑖,∝ , 𝑓𝑖,∝ ] ∝∈[0,1] (3) Where 𝑓̃ is the set of fuzzy valence functions which are made to represent 𝐹̃ (𝑆) for all ∝ ∈ [0,1]. Hence, the ∝-level fragments of 𝑃̅𝑖∝ (‘degree of significance’ for BA) and 𝑒̅𝑖∝ (tolerance zone) for each sustainability criterion can be defined using Eq. 4. Eq. 4 represents the fuzzy valency functions for the sustainability criteria – that is, at the SI’s level (A1 – H4). − ̃+ 𝑓̃𝑖∝ = [𝑓̃𝑖,∝ , 𝑓𝑖,∝ ] = 𝑃̅𝑖∝ − 𝑒̅𝑖∝ + [1, 1] 2 (4) More so, to calculate the fuzzy valency functions at the sustainability indicator levels (A – H), Eq. (5) is employed. Eq. (5) considers the respective 𝑓̃𝑖∝ of the SA (calculated in Eq. (4)) of their corresponding SI – that is, to calculate for A, the 𝑓̃𝑖∝ for attributes A1 – A6 is taken into account in Eq. (5). Where ∫ 𝑓̃𝑑𝑔̃ = ‖ ∝=[0,1] [(𝐶) ∫ 𝑓̃∝− 𝑑𝑔̃∝− , (𝐶) ∫ 𝑓̃∝+ 𝑑𝑔̃∝+ ] + − 𝑔̅𝑖 ∶ 𝑃(𝑆) → 𝐼(𝑅 + ), 𝑔̅𝑖 = [𝑔𝑖− , 𝑔𝑖+ ], 𝑔̅𝑖∝ = [𝑔𝑖,∝ , 𝑔𝑖,∝ ], ̅𝑓𝑖 ∶ 𝑆 → 𝐼(𝑅 + ) (5) and [𝑓̃𝑖− , 𝑓̃𝑖+ ] for i=1,2,3,…, nj. Table 8.3 presents the evaluation results by the GCFI algorithm for ∝= 0 for all the key criteria. The “individual significance” column shows the lowest and the highest value of the “average ‘degree of significance’ value” for the best alternative (𝑇̃𝑖 ) in Table 8.2. For instance, the TFN for criteria “A1” in Table 8.2 (𝑇̃𝑖 ) is (0.516, 0.556, 0.574, 0.584); and the “individual significance” (𝑇𝑖,0 ) for that criterion is obtained as [0.516, 0.584]. Same for the tolerance zone (𝑒𝑖,0 ) of A1 is [0.556, 0.876]. As mentioned, for the SA (A1 – H4), Eq. (3) and (4) is used, while Eq. (5) is used for the SI (A – H). 236 Hence, the fuzzy valence function 𝑓̃𝑖,0 for criteria, A1 is calculated as follows (see Table 8.3): [0.516, 0.584] − [0.556, 0.876] + [1, 1] 2 (0.516 − 0.876 + 1) (0.584 − 0.556 + 1) =[ , ] = [𝟎. 𝟑𝟐𝟎, 𝟎. 𝟓𝟏𝟒] 2 2 𝑓, 𝑓̃(𝐴1) = [𝑓̃(𝐴1),0 ] = 𝑓𝑜𝑟 (∝ = 0) Table 8.3: Evaluation result of the GCFI algorithm for the fuzzy valence function, 𝒇̃∝ 𝒊 for ∝ = 0 ∝ ∝ ̃ ̃ Individual Individual fi Value for the fi Value for the Criteria Criteria significance significance ‘BA’ ‘BA’ code code (𝑇𝑖0 ) (𝑇𝑖0 ) [∫ 𝑓̃𝑑𝑔̃] / (𝑓̃𝑖− , 𝑓̃𝑖+ ) [∫ 𝑓̃𝑑𝑔̃] / (𝑓̃𝑖− , 𝑓̃𝑖+ ) A [0.327, 0.650] F [0.291, 0.639] A1 (0.516, 0.584) (0.320, 0.514) F1 (0.452, 0.512) (0.288, 0.630) A2 (0.423, 0.479) (0.273, 0.534) F2 (0.387, 0.438) (0.255, 0.593) A3 (0.503, 0.570) (0.313, 0.579) F3 (0.484, 0.548) (0.304, 0.648) A4 (0.548, 0.621) (0.336, 0.685) A5 (0.493, 0.559) (0.308, 0.501) G1 (0.447, 0.506) (0.285, 0.548) A6 (0.509, 0.576) (0.316, 0.583) G2 (0.452, 0.511) (0.288, 0.550) G3 (0.387, 0.438) (0.255, 0.514) [0.284, 0.613] B [0.286, 0.607] G B1 (0.452, 0.511) (0.288, 0.550) G4 (0.387, 0.438) (0.255, 0.593) B2 (0.435, 0.493) (0.279, 0.621) G5 (0.447, 0.506) (0.285, 0.548) B3 (0.426, 0.482) (0.275, 0.615) G6 (0.435, 0.493) (0.279, 0.621) [0.287, 0.632] C [0.281, 0.602] H C1 (0.418, 0.473) (0.271, 0.611) H1 (0.458, 0.519) (0.291, 0.554) C2 (0.464, 0.526) (0.294, 0.637) H2 (0.387, 0.438) (0.255, 0.514) C3 (0.442, 0.501) (0.283, 0.625) H3 (0.423, 0.479) (0.273, 0.614) H4 (0.387, 0.438) (0.255, 0.593) [0.260, 0.569] D D1 (0.387, 0.438) (0.255, 0.593) D2 (0.435, 0.493) (0.279, 0.541) D3 (0.258, 0.292) (0.191, 0.441) [0.285, 0.630] E E1 (0.456, 0.516) (0.290, 0.632) E2 (0.452, 0.511) (0.288, 0.630) E3 (0.434, 0.491) (0.279, 0.620) E4 (0.387, 0.438) (0.255, 0.593) 237 More so, Table 8.4 gives the evaluation results by the GCFI algorithm for ∝= 1 for all the key criteria. The ∝= 1, in this case, considers the two median values of the “average ‘degree of significance’ value” for the best alternative (𝑇̃𝑖 ) as presented in Table 8.2 – as the “individual significance” column in Table 8.4. For criteria A1, the “individual significance” (𝑇𝑖,1 ) is [0.556, 0.574] and the tolerance zone (𝑒𝑖,1 ) is [0.589, 0.860]. The fuzzy valence function 𝑓̃𝑖,1 for criteria, A1 is [0.348, 0.492] as calculated using Eq. (3) and (4) (see Table 8.4): 𝑓, 𝑓̃(𝐴1) = [𝑓̃(𝐴1),1 ] = [0.556, 0.574] − [0.589, 0.860] + [1, 1] = [𝟎. 𝟑𝟒𝟖, 𝟎. 𝟒𝟗𝟐] 2 𝑓𝑜𝑟 (∝ = 1) Table 8.4: Evaluation result of the GCFI algorithm for the fuzzy valence function, 𝒇̃∝ 𝒊 for ∝ = 1 ∝ ̃ ̃ Individual fi Value for the Individual fi∝ Value for the Criteria Criteria significance significance ‘BA’ ‘BA’ code code 1 1 − + ̃ ̃ ̃ ̃ (𝑇𝑖 ) (𝑇𝑖 ) [∫ 𝑓 𝑑𝑔̃] / (𝑓𝑖 , 𝑓𝑖 ) [∫ 𝑓 𝑑𝑔̃] / (𝑓̃𝑖− , 𝑓̃𝑖+ ) A [0.357, 0.616] F [0.318, 0.617] A1 (0.556, 0.574) (0.348, 0.492) F1 (0.486, 0.502) (0.313, 0.610) A2 (0.456, 0.471) (0.298, 0.450) F2 (0.417, 0.430) (0.278, 0.574) A3 (0.542, 0.559) (0.341, 0.544) F3 (0.521, 0.538) (0.330, 0.628) A4 (0.591, 0.609) (0.365, 0.664) G [0.312, 0.583] A5 (0.532, 0.549) (0.336, 0.480) G1 (0.482, 0.497) (0.311, 0.513) A6 (0.548, 0.566) (0.344, 0.547) G2 (0.486, 0.502) (0.313, 0.515) B [0.309, 0.589] G3 (0.417, 0.430) (0.278, 0.480) B1 (0.486, 0.502) (0.313, 0.515) G4 (0.417, 0.430) (0.278, 0.574) B2 (0.469, 0.484) (0.304, 0.601) G5 (0.482, 0.497) (0.311, 0.513) B3 (0.459, 0.473) C C1 C2 C3 (0.450, 0.464) (0.500, 0.516) (0.477, 0.492) D1 D2 D3 (0.417, 0.430) (0.469, 0.484) (0.278, 0.287) E1 E2 E3 E4 (0.491, 0.507) (0.486, 0.502) (0.467, 0.482) (0.417, 0.430) D E (0.299, 0.596) [0.313, 0.611] (0.295, 0.591) (0.320, 0.617) (0.308, 0.605) [0.287, 0.538] (0.278, 0.574) (0.304, 0.506) (0.209, 0.408) [0.312, 0.609] (0.315, 0.612) (0.313, 0.610) (0.303, 0.600) (0.278, 0.574) 238 G6 (0.469, 0.484) H1 H2 H3 H4 (0.493, 0.509) (0.417, 0.430) (0.456, 0.471) (0.417, 0.430) H (0.304, 0.601) [0.307, 0.577] (0.316, 0.519) (0.278, 0.480) (0.298, 0.594) (0.278, 0.574) Step 4: Computation of the λ value (for the sustainability indicators, A – H) and the Fuzzy measures g(A(i) ) Meanwhile, to calculate the location value, a λ value (for the indicators, A – H) and the fuzzy measures 𝑔(𝐴(𝑖) ) (for the attributes, A1 – H4), where i=1,2,3, …, n is needed. These are derived using Eq. (6) to (8) as follows: 𝑔(𝐴(𝑛) ) = 𝑔({𝑃(𝑠) }) = 𝑔𝑛 𝑔(𝐴(𝑖) ) = 𝑃𝑖 + 𝑔(𝐴(𝑖+1) ) + λ𝑃𝑖 𝑔(𝐴(𝑖+1) ), 𝑛 1 = 𝑔(𝑆) = 1⁄ {∏[1 + λg(𝐴 )] − 1} 𝑖 λ 𝑖=1 { 𝑛 ∑ 𝑔(𝐴𝑖 ) 𝑖=1 𝑓𝑜𝑟 1 ≤ 𝑖 ≤ 𝑛 𝑖𝑓 λ ≠ 0 (6) (7) (8) 𝑖𝑓 λ = 0 Eq. (8) is used for solving for λ for ∝= [0,1] (see Tables 8.5 & 8.6); where 𝑃̃(𝑠) is the average ‘degree of significance’ value for the highest-ranked fuzzy valence functions 𝑓̃𝑖∝ for each criterion, 𝑖; and 𝑃𝑖 is the regular ‘degree of significance’ value for its corresponding criteria, 𝑖. For instance, as shown in Table 8.5, the λ for criteria A, taking ∝= 0 is calculated to give λ [-0.9963, -0.9997]. The average ‘degree of significance’ value for the subset of criteria ‘A’ (𝑃(𝐴),0) as presented in Table 8.2 (that is, A1 – A6) is used as the fuzzy numbers ‘g’ as shown in Eq. (8) which are [0.660, 0.789], [0.597, 0.736], [0.597, 0.736], [0.528, 0.672], [0.660, 0.789], and [0.597, 0.736] for sustainability attributes A1 – A6 respectively (taking ∝= 0). 1 = 𝑔(𝑆) = 1⁄λ {[(1 + 0.660λ)(1 + 0.597λ)(1 + 0.597λ)(1 + 0.528λ)(1 + 0.660λ)(1 + 0.597λ)] − 1} 𝑓𝑜𝑟 ∝= 0 and λ− + 0.736λ)] − 1} 𝑓𝑜𝑟 ∝= 0 and λ+ 1 = 𝑔(𝑆) = 1⁄λ {[(1 + 0.789λ)(1 + 0.736λ)(1 + 0.736λ)(1 + 0.672λ)(1 + 0.789λ)(1 The two solutions give: λ = [−𝟎. 𝟗𝟗𝟔𝟑, −𝟎. 𝟗𝟗𝟗𝟕] 𝑓𝑜𝑟 ∝= 0. Hence, See Tables 8.5 and 8.6 for the λ values for the sustainability indicators (A – H) for ∝= [0,1] respectively. 239 More so, to calculate the fuzzy measures g(A(i) ), for 1=1, 2, 3, …, n for the sustainability attributes (A1 – H4); Eq. (6) and (7) are employed and results presented in Tables 8.5 and 8.6 for ∝= [0,1] respectively. Thus, to calculate the g(A(i) ) for SA (A1 – A6) of its corresponding SI ‘A’ – the average ‘degree of significance’ value (𝑃̃𝑖 ) for the SA, as presented in Table 8.2, is used as shown in Eq. (6) and (7). However, to calculate the fuzzy measures g(A(i) ), for SA (A1 – A6), the fuzzy + − valence functions [fi,∝ , fi,∝ ] as calculated in Tables 8.3 and 8.4 is sorted from high to low; the same approach was adopted to evaluate for the other SA (i.e., B1 – H4) as presented in Tables 8.5 and 8.6. Hence, to deduce the g(A(i) ) for criteria A1 – A6 − and taking ∝=0; its fi,0 is sorted as follows: − − − − f(A4),0 = 0.336 > f(A1),0 = 0.320 > f(A6),0 = 0.316 > f(A3),0 = 0.313 − − > f(A5),0 = 0.308 > f(A2),0 = 0.273 − The corresponding average ‘degree of significance’ value, 𝑃i,0 (see Table 8.2) to − these 𝑓𝑖,0 values can be given as: − − − − 𝑃(A4),0 = 0.528, 𝑃(A1),0 = 0.660, 𝑃(A6),0 = 0.597, 𝑃(A3),0 = 0.597 − − 𝑃(A5),0 = 0.660, 𝑃(A2),0 = 0.597 The earlier calculated λ− is -0.9963 using Eq. (8). Then, taking ∝= 0, the fuzzy measures 𝑔− (A(i) ) for A1 – A6 can be calculated (see Table 8.5) using Eq. (6) and (7) as follows: λ− = −0.9963 − 𝑔− (A(A4) ) = 𝑃(A4) = 0.528 − − + 𝑔− (𝐴(𝐴4) ) + λ− 𝑃(A1) 𝑔− (𝐴(𝐴4) ) = 0.841 𝑔− (A(A1) ) = 𝑃(A1) − − 𝑔− (A(A6) ) = 𝑃(A6) + 𝑔− (𝐴(𝐴1) ) + λ− 𝑃(A6) 𝑔− (𝐴(𝐴1) ) = 0.938 − − 𝑔− (A(A3) ) = 𝑃(A3) + 𝑔− (𝐴(𝐴6) ) + λ− 𝑃(A3) 𝑔− (𝐴(𝐴6) ) = 0.977 − − 𝑔− (A(A5) ) = 𝑃(A5) + 𝑔− (𝐴(𝐴3) ) + λ− 𝑃(A5) 𝑔− (𝐴(𝐴3) ) = 0.995 − − 𝑔− (A(A2) ) = 𝑃(A2) + 𝑔− (𝐴(𝐴5) ) + λ− 𝑃(A2) 𝑔− (𝐴(𝐴5) ) = 1.000 240 Table 8.5: For ∝ = 0, fuzzy measures g(A(i) ) and 𝝀 values Fuzzy Fuzzy Criteria Criteria − + measures measures 𝛌 𝛌 code code 𝒈− (𝑨(𝒊) ) 𝒈+ (𝑨(𝒊) ) -0.9963 A -0.9997 F − 𝛌 -0.8157 Fuzzy measures 𝒈− (𝑨(𝒊) ) + 𝛌 -0.9536 Fuzzy measures 𝒈+ (𝑨(𝒊) ) A1 0.8407 0.9990 F1 0.8289 0.9132 A2 1.0000 0.9942 F2 1.0000 0.6718 A3 0.9770 0.9772 F3 0.5283 1.0000 A4 0.5283 0.6718 A5 0.9945 1.0000 G1 0.9384 0.9987 A6 0.9377 0.9133 G2 0.5974 0.9744 G3 0.9928 1.0000 -0.8511 B -0.9943 G -0.9638 -0.9995 B1 0.5974 1.0000 G4 1.0000 0.9018 B2 0.8289 0.6718 G5 0.8400 0.9936 B3 1.0000 0.9086 G6 0.9738 0.6718 -0.8157 C -0.9536 -0.9546 H -0.9919 C1 1.0000 1.0000 H1 0.5974 0.9779 C2 0.5283 0.6718 H2 0.8244 1.0000 C3 0.8289 0.9132 H3 0.9369 0.6718 H4 1.0000 0.8959 -0.8793 D -0.9718 D1 0.8482 0.6718 D2 0.5974 0.9272 D3 1.0000 1.0000 -0.9342 E -0.9871 E1 0.5283 0.6718 E2 0.7958 0.8980 E3 0.9314 0.9743 E4 1.0000 1.0000 241 Table 8.6: For ∝ = 1, fuzzy measures g(A(i) ) and 𝝀 values Fuzzy Fuzzy Criteria Criteria − + measures measures 𝛌 𝛌 code code 𝒈− (𝑨(𝒊) ) 𝒈+ (𝑨(𝒊) ) -0.9984 A -0.9992 F − 𝛌 -0.8802 Fuzzy measures 𝒈− (𝑨(𝒊) ) + 𝛌 -0.9146 A1 0.8773 0.9977 F1 A2 1.0000 0.9885 F2 1.0000 0.6119 A3 0.9862 0.9618 F3 0.5761 1.0000 A4 0.5761 0.6119 A5 0.9971 1.0000 G1 0.9588 0.9971 A6 0.9579 0.8776 G2 0.6496 0.9595 G3 0.9960 1.0000 -0.9059 B -0.9974 G -0.9335 0.8600 Fuzzy measures 𝒈+ (𝑨(𝒊) ) 0.8813 -0.9987 B1 0.6496 1.0000 G4 1.0000 0.8693 B2 0.8866 0.6119 G5 0.8783 0.9881 B3 1.0000 0.8742 G6 0.9840 0.6119 -0.8802 C -0.9146 -0.9740 H -0.9829 C1 1.0000 1.0000 H1 0.6496 0.9644 C2 0.5761 0.6119 H2 1.0000 1.0000 C3 0.8600 0.8813 H3 0.8612 0.6119 H4 0.6496 0.8557 -0.9259 D -0.9481 D1 0.8792 0.6119 D2 0.6496 0.8990 D3 1.0000 1.0000 -0.9601 E -0.9732 E1 0.5761 0.6119 E2 0.8335 0.8594 E3 0.9486 0.9595 E4 1.0000 1.0000 8.4.1.3 The defuzzification process, normalization process and results Having calculated fuzzy measures g(A(i) ) as a membership function of the TFN (𝑃̃𝑖 ), fuzzy number 𝑃̃𝑖 is defuzzified to 𝑝̃𝑖 using Eq. (9) as presented in step 5 below. Where 𝐴𝑖 ∩ 𝐴𝑗 = 𝜙 for all i, j = 1,2,3, …, n and i ≠ j, λ ∈ (−1, ∞). Let 𝜇 be a fuzzy measure on (𝐼, 𝑃 (𝐼)) and an application 𝑓 ∶ 𝐼 → ℜ+ . The Choquet integral of 𝑓 with respect to 𝜇 is defined by: 242 𝑛 ∫ 𝑓𝑑𝜇 = ∑ (𝑓(𝜎(𝑖)) − 𝑓(𝜎(𝑖 − 1))) 𝜇(𝐴𝑖 ) 𝐼 𝑖=1 Where 𝜎 is a permutation of the indices to have 𝑓(𝜎(𝑖 − 1)) ≤ ⋯ ≤ 𝑓(𝜎(𝑛)), 𝐴𝑖 = {𝜎(𝑖), … , 𝜎(𝑛)} and 𝑥𝑓(𝜎(0)) = 0, by convention. Therefore, the aggregation of the mono-dimensional utility functions of the SA is achieved by using the generalized Choquet integral function, which is defined in terms of: 𝑓 ∶ 𝑆 → [0,1], 0 ≤ 𝑓(𝑠(1) ) ≤ 𝑓(𝑠(2) ) ≤ ⋯ 𝑓(𝑠(𝑛) ) ≤ 1, 𝑓(𝑠(0) ) = 0 and 𝐴(𝑖) = {𝑠(1) , … , 𝑠(𝑛) }. 𝑛 (𝐶) ∫ 𝑓𝑑𝑔 = ∑ (𝑓(𝑠(𝑖) ) − 𝑓(𝑠(𝑖−1) )) 𝑔(𝐴(𝑖) ) 𝑖=1 (9) Step 5: Evaluation of the Fuzzy Valence Functions at the SI level The fuzzy measures g(A(i) ) for SA (A1 – H4) is presented in Tables 8.5 and 8.6; and Eq. (9) is then employed to calculate the fuzzy valency functions (𝑓̃𝑖∝ ) at the SI level (A – H); for ∝= [0,1] as presented in Tables 8.3 and 8.4. The following examples show how the fuzzy valency function (∫ 𝑓̃𝑑𝑔̃) of criteria ‘A’ and other criteria (i.e., B – H) was calculated as presented in Table 8.3, taking ∝= 0. The 𝑔− (𝐴(𝑖) ) for criteria A1 – A6 (see Table 8.5) are first sorted, as shown below: 𝑔− (A(A2) ) = 1.000 > 𝑔− (A(A3) )= 0.977 > 𝑔− (A(A5) ) = 0.995 > 𝑔− (A(A6) ) = 0.938 > 𝑔− (A(A1) ) = 0.841 > 𝑔− (A(A4) ) = 0.528, − Meanwhile, the corresponding fuzzy valence functions 𝑓𝑖,0 (see Table 8.3) to these 𝑔− (𝐴(𝑖) ) values are: − − − − f(A2),0 = 0.273, f(A3),0 = 0.313, f(A5),0 = 0.308, f(A6),0 = 0.316, − − f(A1),0 = 0.320, f(A4),0 = 0.336 The (∫ 𝑓̃𝑑𝑔̃) of criteria ‘A’ is given as: 243 ∫ 𝑓̃0− 𝑑𝑔̃0− = {[1.000 𝑋 0.273] + [0.977 𝑋 (0.313 − 0.273)] + [0.995 𝑋 (0.308 − 0.313)] + [0.938 𝑋 (0.316 − 0.308)] + [0.841 𝑋 (0.320 − 0.316)] Similarly, + [0.528 𝑋 (0.336 − 0.320)]} = 𝟎. 𝟑𝟐𝟕 ∫ 𝑓̃0+ 𝑑𝑔̃0+ = 𝟎. 𝟔𝟓𝟎 Hence, ∫ 𝑓̃𝑑𝑔̃ = [0.327, 0.650] 𝑡𝑎𝑘𝑖𝑛𝑔 ∝= 0; for criteria A (see Table 8.3) The respective SA are aggregated into their corresponding individual SI, using a hierarchical process by applying the two-stage aggregation process of the Choquet fuzzy integral (Eq. (9)). The resultant value at the SI level yields a fuzzy number, 𝑉̃ such that using the Choquet fuzzy integral, we have the generalized Choquet integral (Eq. (10)): 𝑉̃ = (𝐶) ∫ 𝑓𝑑𝑔 (10) ̃ ) for the key Step 6: Defuzzification of the Choquet integral values (𝑽 sustainability criteria – at the SA and SI levels Assume that the membership of 𝑉̃ is as defined in Eq. (10) (Kurt, 2014; Ozdemir & Ozdemir, 2018) and presented in Table 8.7; the fuzzy number 𝑉̃ can be defuzzied into a crisp value 𝑣 using Eq. (11) for both levels of the SA and SI (Table 8.7). 𝐹(𝐴̃) = 𝑣1 + 𝑣2 + 𝑣3 + 𝑣4 4 (11) In Table 8.7, using the calculation of the generalized Choquet integral (Eq. (10)), the weightings of each sustainability criteria (SA & SI alike) are obtained. Also, the defuzzified overall values, 𝐹(𝐴̃) for the sustainability criteria using the generalized Choquet fuzzy integral is presented within the same table. For instance, the value (0.488) for criteria “A” in Table 8.7 is obtained in a similar way using Eq. (11). 0.327 + 0.357 + 0.616 + 0.650 = 𝟎. 𝟒𝟖𝟖 4 244 Step 7: Computing the Normalized Weights for the key sustainability criteria (SA and SI levels) – for the building classification types (NB & EB) After the defuzzification procedure, the normalization of the resulting defuzzified value 𝐹(𝐴̃) was calculated to get the final weight of each sustainability criteria (Byun & Lee, 2005; Ertuǧrul & Karakaşoǧlu, 2008; Kahraman et al., 2008; Pramanik et al., 2017). Eq. (12) was utilized to normalize the 𝐹(𝐴̃) for all criteria for both new and existing buildings. Note: for all computation of values for the ‘existing building’ classification – the sustainability indicator “A,” which is the “sustainable construction practices” and its subsets factors are excluded. The resulting value is then normalized weight 𝑁(𝐴̃) for the criterion (Table 8.7) and the summation of all the 𝑁(𝐴̃) for the SI (A – H) as well as the SA for each corresponding SI (e.g., A1 – A6) is equal to one. 𝑁(𝐴̃) = Where i = 1,2,3, …, n 𝐹(𝐴(𝑖) ) 𝑛 ∑𝑖=1 𝐹(𝐴(𝑖) ) (12) For instance, to calculate the 𝑁(𝐴̃) for criterion “B1” which is given as 0.3170 in Table 8.7, using Eq. (12). We have: 𝑁(𝐴̃(𝐵1) ) = 0.417 = 𝟎. 𝟑𝟏𝟕𝟎 0.417 + 0.451 + 0.446 Similarly, for criterion “C” for existing building (EB), the 𝐹(𝐴(𝑖) ) of criteria B – H are aggregated as the ∑𝑛𝑖=1 𝐹(𝐴(𝐵−𝐻) ): 𝑁(𝐴̃(𝐶) ) = 0.461 = 𝟎. 𝟏𝟒𝟔𝟗 0.449 + 0.461 + 0.413 + 0.459 + 0.466 + 0.477 + 0.442 Step 8: Determination of the Global Weights for the key sustainability criteria (SA) – for the building classification types (NB & EB) As earlier mentioned, the proposed criteria BSAM scheme consists of a three hierarchical structure of sustainability indicators (SI), sustainability attributes (SA), and sustainability sub-attributes (SSA). Having computed the normalized weights for the SI (A – H) and their respective SA (A1 – H4) as presented in step 7, the global weight (WG) which is a critical variable in the sustainability assessment process can 245 be calculated. The WG is the product of the 𝑁(𝐴̃) of the SA and the 𝑁(𝐴̃) of its corresponding SI, as illustrated in Eq. (13). 𝑊𝐺𝑗 = 𝑁(𝐴)𝑗 × 𝑁(𝐴)𝑖 (13) Where 𝑊𝐺𝑗 = global weight of the jth sustainability attribute. 𝑁(𝐴)𝑗 = normalized weight of the jth sustainability attribute. 𝑁(𝐴)𝑖 = corresponding normalized weight of the ith sustainability indicator for the jth sustainability attribute Hence, the 𝑊𝐺(𝐴1) for criteria “A1” is: 𝑊𝐺(𝐴1) = 𝑁(𝐴)(𝐴1) × 𝑁(𝐴)(𝐴) = 0.1591 × 0.1345 = 𝟎. 𝟎𝟐𝟏𝟒 Table 8.7: Defuzzification and normalization results for the sustainability criteria (SI & SA) using GCFI method Weights Weights Normalized Normalized Defuzzied global global Fuzzy numbers for the weights – weights – Criteria Value (SA) – (SA) – for ‘best alternative’ for NB for EB code ̃) ̃ for NB EB 𝑭(𝑨 (𝑽) ̃ ̃) 𝑵(𝑨) 𝑵(𝑨 𝑾𝑮𝒋 𝑾𝑮𝒋 A (0.327, 0.357, 0.616, 0.650) 0.488 0.1346 A1 (0.320, 0.348, 0.492, 0.514) 0.419 0.1591 0.0214 - - A2 (0.273, 0.298, 0.450, 0.534) 0.401 0.1526 0.0205 - - A3 (0.313, 0.341, 0.544, 0.579) 0.444 0.1690 0.0227 - - A4 (0.336, 0.365, 0.664, 0.685) 0.512 0.1948 0.0262 - - A5 (0.308, 0.336, 0.480, 0.501) 0.406 0.1545 0.0208 - - A6 (0.316, 0.344, 0.547, 0.583) 0.447 0.1701 0.0229 - - B (0.284, 0.309, 0.589, 0.613) 0.449 0.1238 B1 (0.288, 0.313, 0.515, 0.550) 0.417 0.3170 0.0392 0.3170 0.0454 B2 (0.279, 0.304, 0.601, 0.621) 0.451 0.3435 0.0425 0.3435 0.0491 B3 (0.275, 0.299, 0.596, 0.615) 0.446 0.3395 0.0420 0.3395 0.0486 C (0.287, 0.313, 0.611, 0.632) 0.461 0.1271 C1 (0.271, 0.295, 0.591, 0.611) 0.442 0.3240 0.0412 0.3240 0.0476 C2 (0.294, 0.320, 0.617, 0.637) 0.467 0.3424 0.0435 0.3424 0.0503 C3 (0.283, 0.308, 0.605, 0.625) 0.455 0.3336 0.0424 0.3336 0.0490 D (0.260, 0.287, 0.538, 0.569) 0.413 0.1141 246 0.1431 0.1469 0.1318 Weights global (SA) – for NB 𝑾𝑮𝒋 0.0424 Weights Normalized global weights – (SA) – for for EB EB ̃) 𝑵(𝑨 𝑾𝑮𝒋 0.3713 0.0489 D1 Fuzzy numbers for the ‘best alternative’ ̃ (𝑽) (0.255, 0.278, 0.574, 0.593) Defuzzied Value ̃) 𝑭(𝑨 0.425 Normalized weights – for NB ̃) 𝑵(𝑨 D2 (0.279, 0.304, 0.506, 0.541) 0.408 0.3562 D3 (0.191, 0.209, 0.408, 0.441) 0.312 0.2725 E (0.285, 0.312, 0.609, 0.630) 0.459 0.1267 E1 (0.290, 0.315, 0.612, 0.632) 0.463 0.2572 0.0326 0.2572 0.0376 E2 (0.288, 0.313, 0.610, 0.630) 0.460 0.2558 0.0324 0.2558 0.0374 E3 (0.279, 0.303, 0.600, 0.620) 0.450 0.2505 0.0317 0.2505 0.0367 E4 (0.255, 0.278, 0.574, 0.593) 0.425 0.2365 0.0299 0.2365 0.0346 F (0.291, 0.318, 0.617, 0.639) 0.466 0.1286 F1 (0.288, 0.313, 0.610, 0.630) 0.460 0.3376 0.0434 0.3376 0.0502 F2 (0.255, 0.278, 0.574, 0.593) 0.425 0.3120 0.0401 0.3120 0.0464 F3 (0.304, 0.330, 0.628, 0.648) 0.477 0.3504 0.0451 0.3504 0.0521 G (0.286, 0.312, 0.583, 0.607) 0.447 0.1232 G1 (0.285, 0.311, 0.513, 0.548) 0.414 0.1655 0.0204 0.1655 0.0236 G2 (0.288, 0.313, 0.515, 0.550) 0.417 0.1664 0.0205 0.1664 0.0237 G3 (0.255, 0.278, 0.480, 0.514) 0.382 0.1525 0.0188 0.1525 0.0217 G4 (0.255, 0.278, 0.574, 0.593) 0.425 0.1699 0.0209 0.1699 0.0242 G5 (0.285, 0.311, 0.513, 0.548) 0.414 0.1655 0.0204 0.1655 0.0236 G6 (0.279, 0.304, 0.601, 0.621) 0.451 0.1803 0.0222 0.1803 0.0257 H (0.281, 0.307, 0.577, 0.602) 0.442 0.1219 H1 (0.291, 0.316, 0.519, 0.554) 0.420 0.2512 0.0306 0.2512 0.0354 H2 (0.255, 0.278, 0.480, 0.514) 0.382 0.2283 0.0278 0.2283 0.0322 H3 (0.273, 0.298, 0.594, 0.614) 0.445 0.2661 0.0324 0.2661 0.0375 H4 (0.255, 0.278, 0.574, 0.593) 0.425 0.2544 0.0310 0.2544 0.0358 Criteria code 0.3713 0.0406 0.0311 0.3562 0.0469 0.2725 0.0359 0.1463 0.1486 0.1425 0.1409 Note: Sustainability Indicator (SI) levels (bolded values); SA – Sustainability attributes levels; NB – New Buildings; EB – Existing Buildings. Step 9: Determination of the credit points for the key sustainability criteria (SSA & SA levels) Each of the proposed sustainability attributes (SA) has corresponding sub-factors which are subsets of the SA – the sub-attributes (SSA), and each of these SSA has 247 a certain available credit point to be achieved. These credit points (CP) of the respective SSA was determined via the consultation with the 189 invited experts for this study. Therefore, to determine the maximum credit points (CP) of the respective SA, the CP of its related SSA is aggregated, as shown in Eq. (14) modified from Mahmoud et al. (2019). 𝑛 (14) 𝐶𝑃𝑗 = ∑ 𝐶𝑃(ℎ) ℎ=1 Where h = 1,2,3, …, n; 𝐶𝑃(ℎ) = credit points for the related SSA of the jth sustainability attribute; and 𝐶𝑃𝑗 = maximum credit points for the jth sustainability attribute. 8.4.1.4 Establishing the Building Sustainability Evaluation Index (BSEI) and BSER Furthermore, the building sustainability evaluation index (BSEI), which is the aggregation of all the factor indices (FI) of all the sustainability attributes (SA), is calculated using Eq. (15) which is modified from Mahmoud et al. (2019). Moreover, the building sustainability evaluation ratio (BSER), which is the percentage between the BSEI and the maximum BSEI, is useful to determine the scale ranking of the assessed building based on the proposed BSAM certification grade system (see section 8.4.1.5). The maximum BSEI and the BSEI are both derived using Eq. (15); however, for the BSEI, the calculated 𝐶𝑃𝑗 for the SA varies based on the building project evaluated. Meanwhile for the maximum BSEI, its 𝐶𝑃𝑗 is fixed as determined during the experts’ consultations – as the maximum available CP for each SA. The BSER can be deduced using either Eq. (16) or as Eq. (17). 𝑛 𝑛 𝑗=1 𝑗=1 (15) 𝐵𝑆𝐸𝐼 = ∑ 𝐶𝑃𝑗 × 𝑊𝐺𝑗 = ∑ 𝐹𝐼𝑗 𝐵𝑆𝐸𝑅 (%) = 𝐵𝑆𝐸𝑅 (%) = 𝐵𝑆𝐸𝐼 × 100 𝐵𝑆𝐸𝐼𝑚𝑎𝑥 ∑𝑛𝑗=1 𝐶𝑃𝑗 × 𝑊𝐺𝑗 ∑𝑛𝑗=1(𝐶𝑃𝑗 ) × 𝑊𝐺𝑗 𝑚𝑎𝑥 × 100 𝑶𝑹 248 ∑𝑛𝑗=1 𝐹𝐼𝑗 ∑𝑛𝑗=1(𝐹𝐼𝑗 ) 𝑚𝑎𝑥 (16) × 100 (17) 8.4.1.5 Building sustainability grade determination The final stage in the methodological approach is the developing of a grading (ranking) system for the BSAM scheme which is based on (i) the input by the experts who participated in this study; and (ii) a review of the widely used and existing green rating systems such as LEED, BREEAM, BEAM Plus, etc. The experts were asked to supply a range of values from (0) to (100) to represent the grades of sustainability performance of buildings “(i.e., outstanding, excellent, very good, good, acceptable, and unclassified), see Appendix C4. The proposed BSAM certification grade system is a scale from (0) to (100) which accommodates the six sustainability certification grades. Figure 8.5 shows the six certification grades and their respective BSER values (grade 1= unclassified; 2= acceptable; 3= good; 4= very good; 5= excellent; 6= outstanding). BSAM Six-grade certification system Minimum threshold Maximum threshold 100 100 90 81 80 BSER values (%) 73 72 70 63 62 60 82 55 54 50 40 40 39 30 20 10 0 0 1 2 3 4 5 Sustainability grade levels Figure 8.5: BSAM Six-grade certification system: showing the grade levels and corresponding BSER values 249 6 Therefore, for a building to be green certified using the BSAM scheme, it must a minimum BSER value of 40% (i.e., an ‘acceptable’ sustainability grade level). 8.4.2 Implementation of the BSAM scheme: Case study validation The BSEI and BSER values which are computed based on the weights of the sustainability criteria – SI, SA & SSA of the BSAM scheme; as well as the proposed BSAM scheme was implemented in four case studies to demonstrate its usefulness in practice in the built environment. 8.4.2.1 Case study projects descriptions and data The four case studies include two building projects that were classified as “new buildings” (NB) based on BRE (2018) classification, which defined it – buildings of less than one year of occupancy. The four case studies are situated in Nigeria. The other case studies are classified as “existing buildings” (EB), which are buildings of at least one year of occupancy (BRE, 2018). Firstly, the NB case studies comprise of two buildings – a residential facility (CE duplex) and a commercial facility (RA labs). CE duplex is a one-story residential duplex building situated in the south-eastern region of Nigeria with a gross area of 459.820m2 that accommodates seven rooms of different sizes, a stair hall, and other regular residential facilities, a gatehouse among others. It has a green area of 183.928m2 (40% of the GFA) and a paved area of 141.483m 2. More so, the RA lab is a one-story commercial facility situated in the south-western part of Nigeria with a gross area of 346.784m2. It includes four offices and research labs, stores and other facilities on the ground floor, and two offices, meeting halls, a large conference hall, and other facilities on the first floor. It has a green area of 34.581m 2 (10% of the GFA). Secondly, the EB case studies are two residential building projects (SNN building & FT building) situated in Lagos, Nigeria. Both sets of buildings are one-story buildings composed of two units of duplex apartments. The SNN building has a gross area of 896.041m2 consisting of sixteen rooms, two stair halls, other regular residential facilities, and a gatehouse. It has a paved area of 420.064m 2 and a green area of 89.604m2 (10% of the GFA). The FT building has a gross area of 506.509m 2, which accommodated 14 rooms of varying sizes and purposes, two stair halls, and other 250 regular residential facilities, a gatehouse among others. It has a paved area of 101.403m2 and a green area of 202.581m2 (40% of the GFA). Relevant data such as the BIM model and CAD drawings of the case studies were secured to assist in the sustainability assessment of the buildings. Other related documents included site maps, transportation routes, building specifications, utility records (e.g., energy, water, waste, etc.) among others. Meanwhile, necessary assumptions were made where data could not be sourced (Mahmoud, 2017). The BSAM scheme documentation (Olawumi & Chan, 2019c) forms an integral part of the assessment process. 8.4.2.2 Evaluation of the sustainability performance of the case study projects The weights of each SI and SA, BSEI values, BSER values and sustainability grades for the case studies are determined based on (1) the collected data of the four case studies – including the BIM output and other necessary simulations; (2) utilizing the sustainability evaluation equations from Eq. (13) – (17). The entire sustainability evaluation process, the respective weights for each criterion, and the sustainability index (BSEI) are illustrated in Table 8.8 for the NB case studies and Table 8.9 for the EB case studies. These tables also present the BSER determination for the four case studies based on their BSEI, respectively. Each table provides the (i) the description of the sustainability indicators and attributes; (ii) the normalized (local) weights, 𝑁(𝐴̃) of the SI and SA; (iii) the global weights, 𝑊𝐺𝑗 determination for the SA. (iv) credit points, 𝐶𝑃𝑗 determination for the case studies; (v) the sustainability factor index, 𝐹𝐼𝑗 of each criterion. (vi) The BSEI of the case studies; and (vii) the BSER of the four case studies. The credit points, 𝐶𝑃𝑗 and sustainability factor index, 𝐹𝐼𝑗 of each criterion are subdivided into attained and maximum segments; the attained points and indices are the current evaluation of the case study building, whereas the maximum segment represents a 100% score that can be awarded to the criterion. 251 Table 8.8: Building Sustainability Evaluation Index (BSEI) – for the two New Buildings Case Studies Sustainability Indicators (SI) SI Normalized Weight ̃) 𝑵(𝑨 Sustainability Attributes (SA) SA Normalized Weight ̃) 𝑵(𝑨 SI Global Weight 𝑾𝑮𝒋 0.1346 A1 A2 A3 A4 A5 A6 0.0214 0.0205 0.0227 0.0262 0.0208 0.0229 15.5 6 4 5 3.5 3.5 17 8 7 14 11 12 Site and Ecology (B) 0.1238 B1 B2 B3 0.3170 0.3435 0.3395 0.0392 0.0425 0.0420 1.5 4 2.5 Energy (C) 0.1271 C1 C2 C3 D1 D2 D3 0.3240 0.3424 0.3336 0.3713 0.3562 0.2725 0.0412 0.0435 0.0424 0.0424 0.0406 0.0311 Water (D) 0.1141 Material and Waste (E) 0.1267 E1 E2 E3 E4 0.2572 0.2558 0.2505 0.2365 Transportation (F) 0.1286 F1 F2 F3 Indoor Environmental Quality (IEQ) (G) 0.1232 Building Management (H) 0.1219 Sustainable Construction Practices (A) CE duplex building Maximum Factor Credit Point Index (CP) (FI) RA labs building Maximum Factor Credit Index Point (CP) (FI) Maximum Factor Index (FI)max Attained Credit Point (CP) 0.3318 0.1232 0.0909 0.1310 0.0728 0.0801 0.3640 0.1642 0.1591 0.3669 0.2286 0.2747 14 5 6.5 13 5 7 17 8 7 14 11 12 0.2997 0.1026 0.1478 0.3407 0.1039 0.1602 0.3640 0.1642 0.1591 0.3669 0.2286 0.2747 3 7 5 0.0589 0.1701 0.1051 0.1178 0.2977 0.2102 2 4.5 1 3 7 5 0.0785 0.1914 0.0420 0.1178 0.2977 0.2102 6.5 9 4.5 3 5 2.5 10 10 12 6 6 6 0.2677 0.3917 0.1908 0.1271 0.2031 0.0777 0.4118 0.4352 0.5088 0.2542 0.2438 0.1865 6.5 10 5.5 4 5.5 3 10 10 12 6 6 6 0.2677 0.4352 0.2332 0.1694 0.2235 0.0933 0.4118 0.4352 0.5088 0.2542 0.2438 0.1865 0.0326 0.0324 0.0317 0.0299 5 3 6.5 5 5 4 9 5 0.1629 0.0972 0.2062 0.1497 0.1629 0.1296 0.2855 0.1497 3 1.5 9 4 5 4 9 5 0.0977 0.0486 0.2855 0.1198 0.1629 0.1296 0.2855 0.1497 0.3376 0.3120 0.3504 0.0434 0.0401 0.0451 5 7 2 7 7 2 0.2171 0.2809 0.0901 0.3039 0.2809 0.0901 3 4 1.5 7 7 2 0.1303 0.1605 0.0676 0.3039 0.2809 0.0901 G1 G2 G3 G4 G5 G6 0.1655 0.1664 0.1525 0.1699 0.1655 0.1803 0.0204 0.0205 0.0188 0.0209 0.0204 0.0222 3 6.5 2.5 2.75 8 6 8 8 5 4 9 8 0.0612 0.1334 0.0470 0.0576 0.1632 0.1334 0.1632 0.1641 0.0940 0.0838 0.1836 0.1779 7 8 4 2.5 9 5 8 8 5 4 9 8 0.1428 0.1641 0.0752 0.0524 0.1836 0.1112 0.1632 0.1641 0.0940 0.0838 0.1836 0.1779 H1 H2 H3 H4 0.2512 0.2283 0.2661 0.2544 0.0306 0.0278 0.0324 0.0310 6 1 3 1 13 3 7 3 0.1838 0.0278 0.0973 0.0310 0.3982 0.0835 0.2271 0.0930 10 3 4 2 13 3 7 3 0.3063 0.0835 0.1297 0.0620 0.3982 0.0835 0.2271 0.0930 0.1591 0.1526 0.1690 0.1948 0.1545 0.1701 Attained Credit Point (CP) Building Sustainability Evaluation Index (BSEI) 4.5618 Building Sustainability Evaluation Ratio (BSER) 7.2944 62.54% 252 5.1099 Maximum Factor Index (FI)max 7.2944 70.05% Table 8.9: Building Sustainability Evaluation Index (BSEI) – for the two Existing Buildings Case Studies Sustainability Indicators (SI) Site and Ecology (B) SI Normalized Weight ̃) 𝑵(𝑨 0.1431 Sustainability Attributes (SA) SA Normalized Weight ̃) 𝑵(𝑨 B1 0.3170 B2 0.3435 B3 0.3395 Energy 0.1469 C1 0.3240 (C) C2 0.3424 C3 0.3336 Water 0.1318 D1 0.3713 (D) D2 0.3562 D3 0.2725 Material and 0.1463 E1 0.2572 Waste E2 0.2558 (E) E3 0.2505 E4 0.2365 Transportation 0.1486 F1 0.3376 (F) F2 0.3120 F3 0.3504 Indoor 0.1425 G1 0.1655 Environmental G2 0.1664 Quality (IEQ) G3 0.1525 (G) G4 0.1699 G5 0.1655 G6 0.1803 Building 0.1409 H1 0.2512 Management H2 0.2283 (H) H3 0.2661 H4 0.2544 Building Sustainability Evaluation Index (BSEI) Building Sustainability Evaluation Ratio (BSER) SI Global Weight 𝑾𝑮𝒋 0.0454 0.0491 0.0486 0.0476 0.0503 0.0490 0.0489 0.0469 0.0359 0.0376 0.0374 0.0367 0.0346 0.0502 0.0464 0.0521 0.0236 0.0237 0.0217 0.0242 0.0236 0.0257 0.0354 0.0322 0.0375 0.0358 SNN building Attained Credit Point (CP) Maximum Credit Point (CP) 2 5.5 1 6.5 10 6.5 6 5.5 5.5 4 2.5 6.5 5 5 7 1 7 6.25 3.5 2.25 7.5 5 10 3 4 2 3 7 5 10 10 12 6 6 6 5 4 9 5 7 7 2 8 8 5 4 9 8 13 3 7 3 253 Factor Index (FI) Maximum Factor Index (FI)max 0.0907 0.1361 0.2703 0.3440 0.0486 0.2429 0.3093 0.4758 0.5028 0.5028 0.3185 0.5880 0.2937 0.2937 0.2582 0.2817 0.1975 0.2155 0.1506 0.1882 0.0936 0.1498 0.2383 0.3299 0.1730 0.1730 0.2509 0.3512 0.3246 0.3246 0.0521 0.1041 0.1650 0.1886 0.1482 0.1897 0.0760 0.1086 0.0545 0.0968 0.1768 0.2122 0.1284 0.2055 0.3539 0.4601 0.0965 0.0965 0.1499 0.2624 0.0717 0.1075 4.9933 6.6288 75.33% FT building Attained Credit Point (CP) Maximum Credit Point (CP) 2 4.5 2.5 7.5 10 5.5 3.5 5 5 5 3 8.5 5 4 2.5 0.75 7.5 6.5 3.5 2.75 8 6 8.5 1 4 2 3 7 5 10 10 12 6 6 6 5 4 9 5 7 7 2 8 8 5 4 9 8 13 3 7 3 Factor Index (FI) Maximum Factor Index (FI)max 0.0907 0.1361 0.2211 0.3440 0.1214 0.2429 0.3569 0.4758 0.5028 0.5028 0.2695 0.5880 0.1713 0.2937 0.2347 0.2817 0.1796 0.2155 0.1882 0.1882 0.1123 0.1498 0.3116 0.3299 0.1730 0.1730 0.2007 0.3512 0.1159 0.3246 0.0391 0.1041 0.1768 0.1886 0.1541 0.1897 0.0760 0.1086 0.0666 0.0968 0.1886 0.2122 0.1541 0.2055 0.3008 0.4601 0.0322 0.0965 0.1499 0.2624 0.0717 0.1075 4.6595 6.6288 70.29% 8.4.3 Comparison between the BSAM scheme and other green building rating systems This section highlights the significant improvements made in the development of the BSAM scheme as compared to the other existing green building rating systems. Previous studies (Alwisy et al., 2018; Berardi, 2012; Illankoon et al., 2017; Mahmoud et al., 2019) have reported that the existing green building rating systems such as LEED, BREEAM, etc. places more emphasis on the environmental sustainability criteria and little or no consideration of the economic and social sustainability criteria. Other improvements and precedence of the BSAM scheme over the existing green building rating systems are highlighted in Table 8.8.10 for further illustration. Table 8.10: Precedence of the BSAM scheme over the existing green building rating systems Items Other GBRS BSAM Scheme Reference (inclusive of All Illankoon et al. (2017); Olawumi and Chan (2019c) the GBRS documentations) Inclusive of the 3 pillars of sustainable development – social, economic, and environmental sustainability criteria Mainly environmental criteria Key social sustainability criteria - like education, awareness, stakeholder relation, inclusiveness, employment None In detail Liu et al. (2013); Shari (2011) Key economic sustainability criteria - like local economy, re-use etc. Little or no focus In detail Ali and Al Nsairat (2009); Liu et al. (2013); Wei et al. (2011) Cultural aspect - such as cultural heritage in design None except in BEAM Plus In detail Banani et al. (2013); Salehudin et al. (2012); Shari (2011) Management criterion Little or no focus More focus Illankoon et al. (2017); Olawumi and Chan (2019c); Sev (2009) Material and waste construction phase) criterion (at the Weighting methodology – robustness and capability of the method to express the interaction among the sustainability criteria None “aggregation of points” method – incapable of expressing interaction In detail Olawumi (2019c) GCFI method – see section 8.3.3 Mahmoud (2019) and Chan et Note: GBRS – Green Building Rating System; GBRS documentations - (see BCA, 2015; GBCA, 2017; HKGBC, 2019; IBEC, 2008; IGBC, 2014; USGBC, 2017) 254 al. 8.5 Discussion of findings This section will discuss the results of this study in two aspects: (i) the determined weights of the sustainability indicators (SI) and attributes (SA) based on the application of the generalized Choquet fuzzy integral algorithm (section 8.5.1). (ii) The BSER values and sustainability grade levels of the four case studies (section 8.5.2) based on the determination of the BSEI (model validation). 8.5.1 Weights of the key sustainability criteria The weights of the key sustainability criteria (A – H) are presented in Table 8.7 which are based on the application of the GCFI method on the data collected from the experts’ consultations. When the weights assigned to the sustainability criteria for the BSAM scheme in this study are compared to existing building rating systems such as LEED, BREEAM, BEAM Plus, etc., it reveals differential weighting for the different sustainability criteria. More so, this differential weighting shows a significant variation in the local context of these rating tools. As seen in Table 8.7, the ranking order for the criteria weights is A>F>C>E>B>G>H>D. As earlier mentioned, the BSAM scheme was explicitly developed for countries in the sub-Saharan region of Africa. Meanwhile, as shown in Tables 8.5 and 8.6, the sets of sustainability criteria held interactive characteristics (i.e. share sub-additive effect) as their fuzzy measures are λ < 0. Hence, to improve the overall BSER value of a building project, efforts should be devoted to enhancing the weights of the sustainability indicators, which have higher normalized weights. The sustainable criterion “A” – sustainable construction practices is given the highest priority among the eight criteria by the experts with a value of 0.1346. This criterion comprises several social and economic sustainability sub-criteria such as “ethics & equity,” “societal engagement,” with values of 0.1948 and 0.1526, among others which contributes significantly to its high weight value in comparison to other criteria. Illankoon et al. (2017) reported that the existing green rating tools place very little or no emphasis on social and economic sustainability issues in their assessment of the sustainability performance of building. Criterion “F,” which is transportation receives the next highest priority among the consulted experts with the value of 0.1286, and its sub-criteria of “transport management” and “alternative means of transport” 255 receive the highest weights under this criterion with values of 0.3504 and 0.3376 respectively. The Green Mark green rating tool did not allocate any weights to the transportation criterion (BCA, 2015), while the IGBC allocated just 3% of the total weights to the same criterion (IGBC, 2014). The energy “C” criterion acquired the third highest weight among the sustainability criteria with a value of 0.1271, and its sub-criterion of “energy management” receives the highest weight among the sub-criteria under this criterion with a value of 0.3424. However, according to Illankoon et al. (2017), LEED, BREEAM, Green Star, BEAM Plus gave the highest priority to the energy criterion, which emphasizes the special consideration given to the environmental aspect of sustainability by the existing rating tools and countries in the developed world. The “E-material and waste” criterion receives the fourth priority among the sustainability criteria with a weight value of 0.1267, and its sub-criterion “sustainable purchasing practice” got the highest weight under this criterion with a value of 0.2575. The “B-site and ecology” criterion with a weight value of 0.1238 is the fifth-ranked criterion and its sub-criterion “site management” with a value of 0.3435 is the highest-ranked sub-criterion under the criterion “B.” Among existing green rating tools, these criteria “B” and “E” receive consideration weights allocation (Illankoon et al., 2017). Moreover, the sustainability criteria “IEQ,” “building management,” and “water” with weight values of 0.1232, 0.1219, and 0.1141 respectively received the lowest priority among the criteria as rated by the experts and analyzed using the GCFI method. For the IEQ “G” criterion, its sub-criteria such as “building amenities” and “acoustic performance” with values of 0.1803 and 0.1699 respectively receive the highest weight under the IEQ criterion. For the “H-building management” criterion, its subcriteria “risk management” and “green innovations” with 0.2661 and 0.2544 respectively receive the highest weights. In the “D-water” criterion, its sub-criterion of “water efficiency” with the value of 0.3713 gets the highest weight. An analysis of existing green rating tools by Illankoon et al. (2017) reveals that LEED, Green Mark, BEAM Plus places less consideration for the “building management” criteria, although they place a higher priority on the “water” criterion. 256 8.5.2 Assessment results of the case study projects The results of the evaluation of the sustainability performance of the four building case studies, which include two new and existing buildings, respectively, can be classified under four aspects. The first aspect is the attained credit point (CP) has illustrated in Tables 8.8 and 8.9, which shows the points achieved by the four buildings under each SI based on its related SA. For instance, for sub-criterion “G3thermal comfort”, for the new buildings (NB), the CE duplex and RA lab buildings have a CP value of 2.5 and 4 respectively out of a maximum CP value of 5. Meanwhile, for the existing buildings (EB), the SNN and FT buildings have the same CP value of 3.5 out of 5. The second aspect is the sustainability factor index (FI) of each SI, for example, the 𝐹𝐼 and 𝐹𝐼𝑚𝑎𝑥 of the sub-criterion, “C2” for the CE duplex building are 0.3917 and 0.4352 respectively, as shown in Table 8.8. The third aspect related to the values of the 𝐵𝑆𝐸𝐼 and 𝐵𝑆𝐸𝐼𝑚𝑎𝑥 , these values for the CE duplex building are 4.5618 and 7.2944, respectively as shown in Table 8.8, and for the FT building, the values are 4.6595 and 6.6288 respectively as presented in Table 8.9. Lastly, the fourth aspect under consideration is the BSER value which is calculated based on Eq. (16). The BSER values for the CE duplex and RA lab buildings are 62.54% and 70.05%, respectively, as presented in Table 8.8; where for the SNN and FT buildings, the BSER values are 75.33% and 70.29% respectively as shown in Table 8.9. Therefore, the two new buildings such as CE duplex and RA lab buildings achieved the sustainability grades ‘good’ and ‘very good’ respectively; while the existing buildings – SNN and FT buildings achieved the ‘excellent’ and ‘very good’ sustainable grades respectively based on the BSAM certification grade system illustrated in Figure 8.5 and discussed in section 8.4.1.5. More so, for the new buildings as shown in Table 8.8, the CE duplex and RA lab buildings have different values for the sustainability factor index except for the “energy performance” sub-criterion, which achieved the same 𝐹𝐼. Similarly, for the existing buildings (Table 8.9), the SNN and FT buildings have similar 𝐹𝐼 for the B1, C2, E4, G3, H3, and H4 sub-criteria. Although, for both the new and existing buildings, the summation of the 𝐹𝐼 for each sustainability indicator differs, as shown in Tables 8.8 and 8.9. Furthermore, the BSER values for each sustainability indicator – which is the percentage of the summation of the 𝐹𝐼 for each indicator to the summation of the 𝐹𝐼𝑚𝑎𝑥 as illustrated in Eq. 17 – for the sustainability indicator A – H 257 are 53.28%, 53.4%, 62.7%, 59.6%, 84.65%, 87.14%, 68.75%, 42.4% respectively for the CE duplex building while for the RA labs building the values are 74.15%, 49.86, 69.04%, 71.03%, 75.8%, 53.09, 84.16, 72.53% respectively. The BSER values for criteria “site & ecology,” “material & waste,” and “transportation” for the CE duplex building are higher than the RA labs building, whereas the RA labs building achieved higher BSER values in other criteria. Moreover, for the existing buildings, the SNN buildings achieved higher BSER values for sustainability criteria for “energy,” “water,” “transportation,” and “building management,”; while the FT buildings achieved higher values in the other four sustainability criteria. Consequently, for the new buildings, the CE duplex building has its highest BSER value for the “transportation” criterion, while the RA labs building achieved its highest value for the “IEQ” criterion. Moreover, for the existing buildings, the SNN building has its highest BSER value for the “water” criterion, and the FT building achieved the highest weight for the “material & waste” criterion. Overall, the SNN building achieved the highest sustainability certification grade based on the BSAM scheme and the calculations based on the GCFI algorithm, followed by the FT building, RA labs, and CE duplex buildings respectively. The different percentages of the final BSER for the four building case studies can be attributed to the contrasting weights of its sustainability indicators, attributes, and sub-attributes. As pointed out by Ali and Al Nsairat (2009), Gan et al. (2017), and Illankoon et al. (2017), the weight of each sustainability criteria has a significant impact on the overall sustainability performance of a building 8.6 Conclusions The current study used an MCDM technique, the generalized Choquet fuzzy integral method using TFN to develop a building sustainability evaluation index (BSEI) based on a building green rating system – the BSAM scheme. The data collected from the invited experts were analyzed using the GCFI algorithm. The resulting sustainability index and building classification system was used to assess the sustainability performance of four real-world case studies of building projects. The advantages and superiority of the GCFI over other weighting techniques were discussed in the study. The BSAM scheme which was developed as part of broader research work and more specifically to suit the local context of the sub-Saharan region of Africa 258 presents a more unified sustainability evaluation criteria which comprise the three pillars of sustainability; as compared to the other existing green rating tools such as BREEAM, LEED, etc. The use of the GCFI helped addressed the profound shortcomings in these existing green rating tools which only utilize points aggregation which have been reported in the literature to be an insufficient metric. As it is revealed in the weighting calculation for the respective eight sustainability criteria, significant priority was given to criteria such as sustainable construction practices, transportation, and energy. The “sustainable construction practices” criteria contain a considerable proportion of the social and economic sustainability criteria which were not considered in the existing green building rating tools. Also, the BSAM scheme documentation was developed, which provides comprehensive, and details descriptions of each sustainability criteria, the allocation of points to the criteria, and the various documentary evidence needed to be provided before a criterion can be considered fulfilled by the assessed building project. The practical contributions of the current study to the industry and theoretical standpoints include: (1) Determination of the key decision sustainability criteria which are specific to the sub-Saharan region of Africa (by incorporating the opinions of experts and literature in the selection process). (2) Provided a generic and quantitative system that can aid decision-makers, project teams, and other relevant stakeholders in evaluating the sustainability performance of green buildings. (3) The developed BSAM scheme and its quantitative metrics allows for the comparative assessment of building designs and models which can help stakeholders make informed sustainable decisions. (4) The quantitative evaluation model developed based on the BSAM scheme can help pinpoint aspects in the sustainability performance of buildings that need improvements based on the predefined project’s objectives. (5) Implementing the developed BSAM scheme in building projects can promote greener buildings and sustainable development in the sub-Saharan region of Africa. (6) It contributes to the existing body of knowledge in the field of sustainability – being the first attempt (within the sub-Saharan region) aiming at developing a 259 quantitative green building rating system to enhance sustainability practices in the built environment. A limitation of this study is that the developed BSER and evaluation model are based on the BSAM scheme, which is region-specific. However, the GCFI algorithm can be applied to the other existing green rating tools to determine their BSER values – although these tools focused heavily on only the environmental sustainability construct. For this reason, it is recommended for these green rating tools and future development of regional tools to incorporate the social and economic aspects of sustainable development. For future studies, the GCFI method can be applied to other green rating tools or to evaluate various sustainability issues in the built environment. 8.7 Chapter Summary The need to reduce the impact of building projects on the sustainability of the built environment and improve the use of resources necessitated several interventions such as the development of methods to assess building impacts and improve the sustainability performance of buildings. Using the BSAM scheme – a green building rating system developed specifically for the sub-Saharan region of Africa; the GCFI method was employed to determine the importance weights of the sustainability assessment criteria in this chapter. Data collected from industry experts form the base inputs for the impact of the various sustainability criteria based on the local variations. Consequently, the building sustainability evaluation index and grading scheme were developed to measure and evaluate the sustainability performance of buildings. The developed sustainability rating model was validated in four real-world case studies to demonstrate its usefulness and robustness in practice. The findings revealed that the conventional approach of aggregation of points used by the existing green rating tools is less effective in dealing with criteria that have interactive characteristics. Also, assessment criteria such as sustainable construction practices, transportation, and energy have a significant impact on the sustainability of buildings. The study provides substantial contributions to the existing body of knowledge about green building assessment systems for built environment stakeholders both from the theoretical and practical perspectives. The following chapter focuses on the 260 development of a cloud-based system to enable a dynamic and automated assessment of the sustainability performance of building projects. 261 CHAPTER 9: DEVELOPMENT OF A CLOUD-BASED SUSTAINABILITY DECISION SUPPORT SYSTEM (C-SDSS): A DIGITALIZED AUTOMATED GREEN BUILDING SUSTAINABILITY ASSESSMENT TOOL9 9.1 Chapter Overview The previous chapter employed the generalized Choquet fuzzy integral (GCFI) method – to determine the weights of the sustainability criteria and develop the sustainability evaluation index of the BSAM scheme. The current chapter develop a Cloud-based Sustainability Decision Support System (C-SDSS) to facilitate the assessment of the sustainability performance of green buildings. The C-SDSS platform will be developed using various high-level programming languages such as PHP, Jscript, etc. and relational databases. The primary green building rating system to be used on the C-SDSS platform is the BSAM scheme – which was developed in chapters 7 and 8 and purposely designed specifically for the sub-Saharan region of Africa and which holistically considered the social, economic, and environmental sustainability criteria. Also, the proposed C-SDSS platform will permit the comparison of green building projects’ sustainability credentials on the cloud-based system. 9.2 Development and methodology of C-SDSS platform: Main features and graphical user interfaces (GUI) The C-SDSS platform is a dynamic and automated tool developed in this study with the intent to facilitate the evaluation of the sustainability performance of green buildings within the context of the sub-Saharan region of Africa. The C-SDSS platform as an automated and dynamic digital (web-based) tool is programmed and developed using a high-level language such as PHP (Hypertext Pre-processor), Jscript, and other open-source languages such as HTML, CSS, etc. The C-SDSS platform is regarded as a decision support system as it is designed to aid relevant project stakeholders, and assessors in the evaluation of the sustainability performance of green building projects. Apart from its key functionality to establish the sustainability rating or performance of a building project; the C-SDSS This chapter is fully reported in this working paper: Olawumi, T.O., & Chan, D.W.M. Development of a Cloud-based Sustainability Decision Support System (C-SDSS): A Digitalized Automated Green Building Sustainability Assessment Tool. 9 262 platform has the functionality to compare the sustainability performance of two or more building models or design (as later illustrated via case studies of building projects in Section 9.3.2.3) which can help the users/assessors to decide on the most or better building design or model for a construction project. Also, government agencies or clients can use this ‘compare projects’ functionality of the C-SDSS platform (see Figure 9.3) to decide on the most suitable project bid for their building projects. Also, the developed C-SDSS platform is available for use in any operating system, whether for computers, mobile phones, tablets, etc. since the C-SDSS platform is designed to be accessible by any web browser. The PHP high-level language is used basically to handle the server-side functionalities for the C-SDSS platform while the Jscript handles both the server-side and client-side functionalities. The HTML is a mark-up language while the CSS is a style sheet language. More so, as discussed in chapters 7 and 8, the BSAM scheme – which was developed specifically for the sub-Saharan region of Africa – is the primary green rating system adopted and integrated within the C-SDSS platform. However, the BSAM scheme has a limitation common to the existing green rating tools such as LEED, BREEAM, etc. – which is the use of aggregation of points methodology in their sustainability assessment of green buildings; which does not cater for the interdependence and interactions among the sustainability criteria (Ahmad & Thaheem, 2018; Mahmoud et al., 2019). Hence, to ameliorate this limitation of the BSAM scheme before its integration within the C-SDSS platform developed in this study – the BSAM scheme green rating system’s sustainability assessment algorithm was improved using the Generalized Choquet Fuzzy Integral (GCFI) technique. See Dong et al. (2016), Kurt (2014), and Ozdemir and Ozdemir (2018) for more details on the GCFI methodology and its advantages. Therefore, the GCFI techniques were coded using PHP programming languages during the development of the green building sustainability assessment interfaces of the C-SDSS platform as later discussed in Section 9.2.1.3 and illustrated partly in Figure 9.7. 263 9.2.1 C-SDSS main features The C-SDSS platform was coded on the Adobe Dreamweaver software and hosted on WampServer – which serves as the local server for the C-SDSS during the development phase of the C-SDSS platform. Apart from the Dreamweaver software, WampServer, the C-SDSS, also integrates a relational database – MySQL – which all were used for the coding and optimization process, data entry and storage management, green building sustainability assessment, information and documentation, and output displays. The C-SDSS platform only requires the installation of web-browsers – which comes pre-installed on all mobile devices, desktop computers, and laptops; and it requires access to Wi-Fi access to utilize the dynamic tool. Its functionality is not dependent on the type of web browser used, or any software or operating system deployed. Hence, the C-SDSS platform can be divided into five tiers based on its features as follows. 9.2.1.1 Coding and optimization process The coding, design, and development of the C-SDSS platform is undertaken within the Dreamweaver software using the various programming languages (PHP, Jscript, etc.). Codes were written to design and handle the data entry and storage in the MySQL relational database and to develop the C-SDSS platform – where the actual green building sustainability assessment, display of various outputs, and the documentation of the BSAM scheme is resident. Meanwhile, optimization and verification procedures were undertaken to ensure the various coded and designed features of the C-SDSS platform perform effectively (as designed) on all web browsers such as Google Chrome, Microsoft Edge, Internet Explorer, Mozilla Firefox, Opera, etc. 9.2.1.2 Data entry and storage management This feature links the C-SDSS platform and its process interfaces with the serverbased MySQL databases which allow the input of the various weightings of the BSAM scheme green rating system into the MySQL databases; and also allows for users to enter the relevant project information necessary for the sustainability assessment of such projects as well as the comparative assessment of alternative 264 building designs. Hence, during the automated calculation of the sustainability performance of a green building project, these data in the relational databases are systematically linked and used in the evaluation process. Two main relational databases were used, which has a combined 138 data-filled tables. The first one – with 129 relational tables – stores data relating to the BSAM scheme green rating system such as the weightings for the BSAM criteria (that is, its threelevel hierarchical structure of sustainability indicators, attributes, and sub-attributes); and other results of the GCFI algorithm which were used to calculate the building sustainability evaluation ratio (BSER) of the BSAM scheme. The second database – with nine (9) relational tables – stores information related to the users, project details (description, GFA, owner, project type, etc.), browser cookies, results of the sustainability assessment based on the BSAM scheme, among others. When a project is being registered on the C-SDSS platform, a project identification (ID) is generated for such a project, and the project ID is used in subsequent evaluations of such building project. The data entry feature also includes the ‘user registration’ and ‘sign-in’ pages as well as the ‘project registration’ page (see Figure 9.6). The ‘Delete projects’ page (Appendix E1) also provides an interface for the user or assessor to delete a building project details and sustainability assessment results from the second relational database of the C-SDSS platform. 9.2.1.3 Green building sustainability assessment This feature is one of the key features of the C-SDSS platform as it automatically handles the assessment of the sustainability assessment of the building projects based on the BSAM scheme green rating system. It consists of a few interfaces highlighted and illustrated in Section 9.2.2.2. One of the interfaces, the “Indicator (AH)” (steps D and E, as shown in Figure 9.4) allows the green building project assessor to input values (using the drop-down options list) for the sustainability criteria A-H based on the BSAM scheme documentation (Olawumi & Chan, 2019c) (see Appendix A). Before assessing the sustainability performance of a building project, the user must select the project classification type (step B, Figure 9.4) – whether it is a new building or an existing building – based on BRE (2018) classification. For each sustainability criteria, an excerpt of the BSAM documentation is provided; the excerpts contain a brief description of the sustainability criteria, the 265 total points attainable under the criteria, how the points are allocated, and the relevant documentary evidence required for the attainment of such sustainability criteria of the BSAM scheme. A button, “Submit Assessment” (step D, Figure 9.4) is provided at the end of each web interface of the criteria A-H, once this button is clicked; two project-specific metrics are automatically calculated for each of the sustainability criteria based on the GCFI algorithm (Ozdemir & Ozdemir, 2018) which are – the calculated points and factor indexes. After the building project has been evaluated based on the eight BSAM sustainability criteria; the C-SDSS platform automatically calculates the BSER value, the Building Sustainability Evaluation Index (BSEI) for the green building, its overall certification grade, and plot the line graph (using the project’s sustainability indicators’ BSER), and the gauge graph (using the overall project’s BSER) for the building project (see Figure 9.2). The BSAM scheme green rating scheme (Olawumi & Chan, 2019c) has a six-grade certification system which are Outstanding (82-100); Excellent (73-81); Very good (63-72); Good (55-62); Acceptable (40-54); Unclassified (0-39). 9.2.1.4 Information and documentation This information and documentation feature of the C-SDSS platform provides general information about the overall project aim, scope, and objective; it also details the full documentation of the BSAM scheme green building rating system designed for the sub-Saharan region (including a PDF plug-in for the BSAM scheme documentation) as well as a link to the online repository for the BSAM scheme. This feature also entails the “About” webpage, which provides information about the authors and their profiles, and include a “Contact” page to get necessary feedback about the C-SDSS from the authors. 9.2.1.5 Output displays This C-SDSS key feature display three sets of relevant information and outputs which are presented on the ‘View Project SER,’ ‘Compare Projects,’ and ‘Green projects’ interfaces. The ‘Green projects’ interface (see Figure 9.1) is the main homepage of the C-SDSS platform (see Section 9.2.2.1); once the user sign-in into the C-SDSS, the user will be led to this interface. The ‘Green Projects’ page is populated by building projects which been registered by the user on the C-SDSS 266 platform via the ‘project registration’ interface. For each project registered on the cloud platform, an identifiable project ID is generated for each building project. More so, the ‘Green Projects’ interface has four main gateways (buttons) to the C-SDSS platform, which are the “Assess Project,” “View Project SER,” “Compare Project,” and the “Delete Project(s)” buttons (see Figure 9.4). Each populated and registered building project on the ‘Green Projects’ interface has an attached ‘checkbox’ (on the left-top corner of the building project details) which must be checked to assess the project, view its assessment result, compare the projects, or the delete project(s). If a building project is not checked before clicking on the four main buttons on the ‘Green projects’ interface, an error message is displayed. The ‘View Project SER’ interface (see Figure 9.2) allows the user to view the sustainability assessment result of the ‘checked’ building project. For the user to navigate to this interface, the “View Project SER” button on the ‘Green projects’ interface must have been clicked. On the ‘View Project SER’ interface, relevant sustainability assessment results such as the BSER values for the sustainability indicators and attributes are presented as well as the factor indexes for the subattributes. Also, presented on the ‘View Project SER’ interface is the overall certification grade, the line graph (which is plotted based on the project’s sustainability indicators’ BSER), and the gauge graph (plotted using the overall project’s BSER) of the assessed green building project. 9.2.2 C-SDSS graphical user interfaces (GUI) This section illustrates the various GUI of the C-SDSS main features highlighted and described in Section 9.2.1. 9.2.2.1 Output displays GUI As discussed in Section 9.2.1.5, the output displays feature presents three sets of relevant outputs which are the ‘View Project SER’ (Figure 9.2), ‘Compare Projects’ (Figure 9.3), and ‘Green projects’ outputs pages; of which the ‘Green projects’ is the main homepage (see Figure 9.1) of the C-SDSS platform. 267 Figure 9.1: "Green Projects" GUI Figure 9.2: "View Project SER" GUI 268 Figure 9.3: "Compare Projects” GUI 9.2.2.2 Green building sustainability assessment GUI As expatiated in Section 9.2.1.3, towards assessing the sustainability performance of a building project registered under the C-SDSS platform and based on the developed BSAM scheme for the sub-Saharan region, a number of processes (steps A – G) are involved as illustrated in Figure 9.4. In step A (see Figure 9.4), on the “Green Projects” GUI which displays the C-SDSS registered projects; the user or assessor needs to select the building project to be assessed and clicked the “Assess Project” button which will lead the user to the next interface – “building classification” GUI. At the step B (see Figure 9.4), which is the “building classification” interface; the user selects the building project classification type – whether it is a new building or an existing building – based on BRE (2018) classification; and then the user must click the “Start Assessment” button to navigate to the next stage of assessment. The next phase of assessment (step C) occurs at the “Green Indicators” interface, which contains eight weblinks for the eight sustainability criteria of the BSAM scheme as shown in Figure 9.4. Each link leads the user to eight interfaces (steps D 269 & E) where the building project can be evaluated based on the BSAM documentation (Olawumi & Chan, 2019c). Once a sustainability indicator has been assessed for a building project – say indicator A as shown in step F (Figure 9.4) – a green mark appears in front of the sustainability indicator weblink. As discussed in section 9.2.1.3 and as shown in step E (Figure 9.4), for each sustainability criteria being assessed, an excerpt of the criteria from the BSAM documentation is provided. Figure 9.4: Green Building Sustainability Assessment GUIs 9.2.2.3 Data entry and storage management GUI As discussed in Section 9.2.1.2, these GUI illustrates how the various data entries and their storage within the two MySQL relational databases are integrated with the C-SDSS platform. Figure 9.5 shows the MySQL relational databases of the C-SDSS platform while the “Project Registration” interface – a data entry GUI – is illustrated in Figure 9.6. Once the “Register Project” button on the “Project Registration” GUI is 270 clicked, the building project details are registered in the MySQL database of the CSDSS platform. All the other data entries GUI are illustrated in Appendix E1. Figure 9.5: MySQL relational databases GUI for the C-SDSS platform Figure 9.6: "Project Registration" GUI 271 9.2.2.4 Coding and optimization process GUI As explained in Section 9.2.1.1, the coding, development, and optimization of the CSDSS platform were undertaken within the Adobe Dreamweaver software using PHP (version 5.6), Jscript, and other programming languages. Figure 9.7 shows a part of the coding for the calculation of the fuzzy measures based on the GCFI algorithm which was used to calculate for the BSEI and BSER values of the BSAM scheme green rating system. Appendix E2 reveals the interfaces of other GUI of the coding and optimization process. Figure 9.7: Code reading for the calculation of the fuzzy measures (GCFI algorithm) 9.3 Validation and Verification of the C-SDSS platform This section discusses the verification and validation processes undertaken to confirm the suitability, adequacy, and practicality of the C-SDSS platform as a dynamic and automated tool for the evaluation of the sustainability performance of green buildings in the sub-Saharan region of Africa. As discussed in Section 9.2, the C-SDSS platform consisted of five key features of which the BSAM scheme is the primary green rating system on which the building projects are assessed and 272 certified on the C-SDSS platform. According to Yang et al. (2011), verification means “doing things right,” while validation means “doing the right things.” Hence, both the BSAM scheme and C-SDSS platform verification and validation processes are discussed here – which are (1) the verification of the C-SDSS platform; (2) use of four real-life building project case studies to validate the C-SDSS platform; and (3) Experts’ validation of the C-SDSS platform (inclusive of the BSAM scheme). 9.3.1 Verification of the C-SDSS platform Verification has defined in terms of computerized systems (of which the C-SDSS platform is one) means – ensuring that the codes, design, and program of the developed computerized model and its implementation are appropriate and correct (Kleijnen, 1995; Yang et al., 2011). In verifying, the authors utilize the in-built code verification function of Adobe Dreamweaver software, which helps to check for and locate probable errors in the codes used in the development of the C-SDSS platform. Also, the default error reporting value in the PHP Apache configuration file was not overridden, which allows the authors to effectively review and address likely errors from the coding of the C-SDSS platform using the PHP high-level language. Also, the C-SDSS platform was optimized using appropriate codes to ensure the C-SDSS works effectively and smoothly on every web browsers and operating systems. Finally, the C-SDSS platform codes, interfaces, and design were optimized and verified to ensure they are correct, free of flaws, and errors. 9.3.2 Case study building projects’ validation exercise In order to validate the developed C-SDSS platform and its features, the study utilized four real-life building projects – two of which can be classified as “new buildings” (NB), and the other two can be classified as “existing buildings” (EB) according to BRE (2018) classification. As earlier mentioned, the BSAM scheme is the primary green rating system on the C-SDSS platform (see Section 9.2). More so, the BSAM scheme has eight sustainability indicators (A – H); while, the eight indicators are assessed for new buildings, only sustainability indicators B – H are evaluated for the existing buildings. 273 9.3.2.1 Case study building projects’ descriptions and documentary evidences Table 9.1 shows the descriptions and project details of the four-case study building projects. More so, as part of the requirements to use the BSAM scheme green rating system to assess the sustainability performance of a building project, necessary documentary evidence must be secured (Olawumi & Chan, 2019c). Part of the documentary evidence is the BIM model, CAD drawing, utility records (e.g. energy, water, waste, etc.), building specifications, site layouts, etc. More so, according to Mahmoud et al. (2019) and has evidenced in the sustainability assessment process of the existing green rating systems such as LEED, BREEAM, etc., reasonable assumptions can be made if a piece of documentary evidence is missing. The necessary documentary evidences for the four-case study building projects were secured, and the C-SDSS platform (along with the BSAM scheme) was then used to assess the sustainability performance of the projects, as discussed in Section 9.2.1.3 and Section 9.2.2.2. Table 9.1: Descriptions of the four-case study building projects Description New Buildings Existing Buildings Code CE duplex RA labs SNN building FT building Description One-storey One-storey One-storey One-storey residential building commercial facility buildings (2 units of buildings (2 units of duplexes) Location Anambra Gross Floor Area (GFA, State, Ondo State, southregion, duplexes) Lagos State, south- Lagos State, south- south-eastern western western region, western region, Nigeria Nigeria Nigeria Nigeria 459.820m2 346.784m2 896.041m2 506.509m2 183.928m2 34.581m2 89.604m2 202.581m2 (40% of the GFA) (10% of the GFA) (10% of the GFA) region, m2) Green Area (m2) Paved Area 141.483m 2 2 - 420.064m BSAM-727760-89 BSAM-684201-10 (40% of the GFA) 101.403m2 (m2) Project IDs BSAM-280443-99 (as generated on the C-SDSS platform) 274 BSAM-504397-18 9.3.2.2 Sustainability assessment of the case study projects on the C- SDSS platform The sustainability assessment of the four-case study projects was undertaken on the C-SDSS platform based on the BSAM scheme green rating system. Figures 9.8 and 9.9 reveals the result of the sustainability analysis of the NB and EB case study projects respectively after the “View Project SER” button on the “Green Projects” interface is clicked; while Figure 9.10 revealed the comparative assessment of the case study projects after the “Compare Projects” button on the “Green Projects” interface is clicked – as discussed in Section 9.2.1.5 and Section 9.2.2.1. More so, after these four case study projects were registered on the C-SDSS platform, identifiable project IDs were generated for each of the projects, as presented in Table 9.1. As shown in Figure 9.8, the two NB projects – CE duplex and RA labs have an overall BSER value of 62.54% and 70.05% and certification grade of “Good” and “Very Good” respectively based on the sustainability evaluation result of the two building projects undertaken on the C-SDSS platform (see gauge graphs “C”, Figure 9.8). Similarly, as shown in Figure 9.9, the two EB projects – SNN building and FT building have an overall BSER value of 75.33% and 70.29% and certification grade of “Excellent” and “Very Good” respectively based on the sustainability evaluation result of the two building projects undertaken on the C-SDSS platform (see “C”, Figure 9.9). 275 Figure 9.8: C-SDSS results of the sustainability analysis of the NB case study projects Note: SI: Factor index (project-specific); SImax: Maximum factor index; SER: Sustainability evaluation ratio; Grade: Based on the BSAM scheme certification grade system. More so, to better present the results of the sustainability assessment of the projects, “A” and line graphs “B” in Figures 9.8 and 9.9 reveals the breakdown of the results for each of the sustainability indicators (of the BSAM scheme) for the NB and EB building projects respectively; it shows the BSER value and certification grade for the four building projects’ sustainability indicators as well as a line graph (see “B”, Figures 9.8 & 9.9) plotting the BSER value of the projects’ sustainability indicators (on the x-axis) against the certification grade levels (y-axis). The six shades of color as seen in the gauge graphs “C” in Figures 9.8 and 9.9 are the six-grade certification grade system of the BSAM scheme. 276 Figure 9.9: C-SDSS results of the sustainability analysis of the EB case study projects 9.3.2.3 Comparative sustainability assessment of the case study projects The C-SDSS platform (based on the BSAM scheme green rating system) can be used to compare two green building projects at a time. The comparative assessment of the green building projects on the C-SDSS platform are based on a three-tier of 277 comparison, which are – (1) comparing the factor indexes (SI) values of the sustainability attributes of the green building projects; (2) comparing the BSER values and grades of the sustainability indicators of the building projects (see “A” & line graphs “B”, Figure 9.10); and (3) comparing the overall BSER values and grades for the green building projects (see gauge graphs “C”, Figure 9.10). For the first tier of the comparative assessment of the NB and EB case study projects as revealed in Appendix E3. The two NB case study projects – CE duplex and RA labs – have the same SI values (SI = 0.26766) for the sustainability attribute “C1- Energy Performance”. Also, based on the SI values, the RA labs with 20 sustainability attributes of higher SI is preferable compared to the CE duplex with 11 attributes. Moreover, the EB case study projects – SNN building and FT building – have the same SI values in six sustainability attributes (B1, C2, E4, G3, H3, & H4) with SI values of 0.09070, 0.50282, 0.17301, 0.17301, 0.14991, and 0.07165 respectively. However, for the two EB projects, unlike the NB projects, the EB case study projects have 10 sustainability attributes, each with a higher SI than the other. Hence, no difference between the sustainability performance of the EB projects based on SI’s values of their sustainability attributes. Furthermore, for the second tier of the comparative assessment of the NB and EB case study projects as revealed in “A” and plotted in line graphs “B” in Figure 9.10; the two NB case study projects – CE duplex and RA labs – have different BSER values for the eight sustainability indicators. Hence the building project grades at the indicators’ level were used. As shown in “A” Figure 9.10, the two NB projects have the same certification grade (‘Acceptable’) for sustainability indicator “B- Site and Ecology.” More so, based on the NB project indicators’ grade; RA labs have five indicators (A, C, D, G, & H) with higher sustainability grades than CE duplex building – with just two sustainability indicators (E & F) with higher grades. Hence, the RA labs building has superior sustainability performance than the CE duplex. Moreover, the EB buildings projects – SNN building and FT building – have the same certification grades in three sustainability indicators (B, C, & G); while FT building has only one sustainability indicator (“E- Material and Waste”) with a greater certification grade than the SNN building. The SNN building, meanwhile, has three indicators (D, F, & H) with greater certification grades than the FT building. Hence, the FT building has a lower sustainability performance than the SNN building. 278 Lastly, for the third tier of the comparative assessment of the NB and EB case study projects as revealed in the gauge graphs “C” in Figure 9.10. The two NB case study projects – CE duplex and RA labs – have overall BSER values of 62.54% and 70.05% as well as certification grades of “Good” and “Very Good,” respectively. These results show the RA labs building has a greater overall sustainability performance than the CE duplex building. Meanwhile, for the two EB case study projects – SNN building and FT building – have overall BSER values of 75.33% and 70.29% as well as certification grades of “Excellent” and “Very Good” respectively. The comparative assessment for the EB case studies shows the SNN building with a better sustainability performance than the FT building. Overall, the comparative assessment of the NB and EB case study projects across the three tiers of comparison reveals salient and significant findings at each level of comparative assessment. Across the three-tier of comparative assessment for the NB case study projects, the RA labs building shows increasing record of a higher sustainability performance than the CE duplex building. Meanwhile, for the EB case study projects, the SNN building has a greater sustainability performance than the FT building in the second and third levels of comparative assessment except for the first tier. 279 Figure 9.10: Comparative assessment of the NB and EB cases study projects respectively on the C-SDSS platform In practice, in a case of a building design or model, a client or project team might prefer to use the second tier of comparative assessment (the sustainability indicators) to choose the best building design for their construction project. For instance, for the NB case studies, (let’s assume they are both building designs and not completed projects); although RA labs ‘design’ has the best overall BSER value and grade than the CE duplex ‘design’, the project team or client might prefer the CE duplex ‘design’ if they prefer a building design with better sustainability performance in either “E- Material and Waste” or “F- Transportation” or both. 280 The three-tier comparative assessments obtainable on the C-SDSS platform can provide the project team, client, government agencies, and other key project stakeholders necessary and details information on the sustainability performance or potentials of the green building projects or building designs respectively. 9.3.3 Experts’ validation of the CDSS platform and its integrated BSAM scheme The experts’ validation of the study findings is comprised of two parts – the first one is the developed C-SDSS platform itself, and the second one being the BSAM scheme, which is the primary green rating system used for the sustainability assessment of green buildings on the C-SDSS platform. The BSAM scheme was developed as part of a larger research project. According to Lucko and Rojas (2010), validation of research work is critical and serves as a means to ensures the research process including the methodology conforms to the highest quality standards. Several techniques have been adopted in the extant literature to conduct research validation work, which includes case studies, questionnaire surveys, simulations, experiments, etc. (Hu et al., 2016a; Pyett, 2003; Sousa, 2014). Case studies validation has discussed in section 9.3.2 was earlier carried out while this section will discuss the experts’ validation exercise carried out using expert questionnaire surveys. Meanwhile, according to El-Diraby and O’Connor (2004) there exists no single definition for validation, and per Lucko and Rojas (2010) and Sargent (1991), there is no established methodology to determine the validation approach to be adopted or technique to use. However, according to Law (2007) and Yang et al. (2011), a validation process must be able to affirm that a proposed framework can be a proper and realistic representation of the system in the real-world and/or can predict the real-world performance of the system. The validation process can be classified into six key areas, which are construct, content, internal, external, face, criterion validity (Lucko & Rojas, 2010). The descriptions and details of these validation types are well discussed in extant literature (see Field, 2009; Leedy & Ormrod, 2001; Lucko & Rojas, 2010; Taherdoost, 2016). More so, based on previous studies – four validation types, such as internal, construct, external, and content validity were adopted in designing the validation questionnaire for this study, as discussed in Section 9.3.3.1. 281 9.3.3.1 Validation survey and experts’ demographics Validation questionnaire survey forms were designed to validate the proposed CSDSS platform and its integrated BSAM scheme based on a set of validation statements to validate the suitability, effectiveness, credibility, ease of use, efficiency, usefulness of the C-SDSS platform and the BSAM scheme to facilitate the sustainability assessment of green buildings and promote the implementation of sustainability practices in buildings and real estate development. The invited experts based within the sub-Saharan region of Africa were invited via purposive sampling and snowball sampling techniques. Two modes of questionnaire distribution were adopted – (1) online survey forms; and (2) PDF fill-in survey forms (Olawumi & Chan, 2019a). Links to the online survey and PDF fill-in survey forms were also sent to the experts via email. These modes of questionnaire distribution according to Andrews et al. (2003) helps to saves time and cost. The validation questionnaire comprises of three sections. The first section of the survey form asked for background details from the invited experts. The second section presents the C-SDSS platform template and the weightings of the BSAM scheme criteria (sustainability indicators, attributes, and sub-attributes) as well as the BSER value for an ideal green building (benchmark building). The last section of the survey form presented the eight validation statements or questions which relate to the external, internal, content, and construct validity; and solicits for the experts to rate their level of agreement on these validation questions (VQ) based on a 5-point Likert scale (1= strongly disagree; 3= Neutral; and 5= strongly agree). Key criteria the experts must have before they are invited to participate in the validation exercise include – they must have adequate experience and knowledge in sustainability issues in construction and green building implementation in the built environment. Based on these criteria, the targeted experts who are based within the sub-Saharan region of Africa were identified and sent the survey forms via email. Overall 30 responses were received from the respondents of which 29 responses were found valid – 27 responses were gotten via the online survey form, and two responses were obtained via the PDF fill-in form. The sample size for the validation questionnaire is adequate for further analysis when compared with previous studies such as Ameyaw (2014), Darko (2018), and Osei-Kyei (2018) where 7, 5, and 6 282 respondents participated in these studies’ validation exercises. Table 9.2 shows the background information of the invited experts. Table 9.2: Experts' demographics for validation survey Percentage Percentage Description Frequency Description Frequency (%) (%) Major profession or Years of working occupation experience Architect 2 6.9 Less than 5 years 15 51.7 2 6.9 5 - 10 years 5 17.2 Civil Engineer 6 20.7 11 - 15 years 5 17.2 Electrical Engineer 1 3.4 16 - 20 years 1 3.4 Estate Manager 2 6.9 Above 20 years 3 10.3 Project Manager 6 20.7 Total 29 100 Quantity Surveyor 9 31.0 Urban Planner 1 3.4 Total 29 100 Building Services Engineer From the analysis of the experts’ demographics as presented in Table 9.2, the invited experts are from key professional groups in the built environment and possess adequate years of experience in the subject matter. About 50 percent of the respondents have more than five years of experience in sustainability implementation in building projects; this is due to the slow adoption of sustainability and green buildings in the sub-Saharan region of Africa (Jung & Lee, 2015; Olawumi & Chan, 2018a). The analysis of the invited experts’ demographics lends further credence to the data collated from them for this validation exercise. 9.3.3.2 Validation survey results The results of the analysis of the responses and level of agreement of the invited experts regarding the eight validation questions are presented in Table 9.3. The eight VQs relate to the BSAM scheme green rating system developed as part of same project and which has been integrated with the C-SDSS platform, while three validation statements (VQ4, VQ6, & VQ8) pertains to validating the C-SDSS platform itself. Seven of the validation statements have at least a mean score of 4.00 with only VQ4 with a mean score of 3.97. These mean scores fall within the “very 283 important” classification of Li et al. (2013) mean classification scheme based on a 5point Likert scale. Therefore, based on the results analysis of Table 9.3, it is seen that the four validation areas – external, internal, construct, and content validity – is adequate. VQ1 and VQ8 address external validity, while VQ7 relates to content validity. Meanwhile, VQ4 and VQ6 measure internal validity and VQ2, VQ3, and VQ5 refer to the construct validity (Table 9.3). The two validation statements (VQ4 and VQ6) that address the internal validity as presented in Table 9.3 have an average mean score of 4.02. VQ4 with a mean score of 3.97 certified that the developed BSAM scheme as well the C-SDSS templates and platform are easily understandable to the users (or green building assessors), and it is easy to use and deploy in practice within the context of the sub-Saharan region. Meanwhile, the VQ6 with a mean index of 4.07, the experts confirm that the development of the C-SDSS platform and its integrated BSAM scheme sufficiently address the objectives of the study. As regards the external validity, its two validation statements (VQ1 and VQ8) have an average mean score of 4.14; and most notably, both validation statements have a mean index of 4.14 each. For VQ1, the experts affirm that the identified sustainability indicators (of the BSAM scheme) adopted to achieve a holistic evaluation of the sustainability performance of buildings within the context of sub-Saharan region, is very reasonable. Also, for VQ8, the experts adjudge the suitability and adequacy of the BSAM scheme and the C-SDSS platform to assess the sustainability performance of green buildings to be very high. 284 Table 9.3: Validation survey results of the C-SDSS platform and the BSAM scheme BSAM C-SDSS Code Validation statements/questions Mean scheme platform VQ1 The identified sustainability indicators (of the BSAM scheme) adopted to achieve a holistic evaluation of the sustainability performance of buildings is 4.14 √ 4.31 √ 4.17 √ 3.97 √ 4.03 √ 4.07 √ 4.21 √ 4.14 √ reasonable. VQ2 The identified sustainability attributes within each sustainability indicator (of the BSAM scheme) are adequate and appropriate. VQ3 The identified sustainability sub-attributes within each sustainability attribute are adequate and appropriate. VQ4 The developed BSAM scheme and the C-SDSS templates are easily understandable and easy to use √ in practice. VQ5 The BSAM scheme is inclusive, comprehensive, and of a logical structure. VQ6 The BSAM scheme and the C-SDSS platform sufficiently address the objectives of the study. VQ7 √ The appropriate use of the BSAM scheme would lead to a successful implementation of sustainability in buildings. VQ8 The BSAM scheme and the C-SDSS platform are suitable and adequate to assess holistic sustainability √ of buildings. Note: √ – implies the validation statement applies to either the BSAM scheme and/or the CSDSS platform. Meanwhile, three validation questions (VQ2, VQ3, and VQ5) relating to the construct validity were asked the experts, which resulted in an average mean score of 4.17. VQ2, with a mean value of 4.31, affirmed the appropriateness and adequacy of the identified sustainability attributes within each sustainability indicator of the BSAM scheme. More so, VQ3 has a mean index of 4.17, which certifies that the identified sustainability sub-attributes within each sustainability attribute are very adequate and appropriate within the context of sub-Saharan region. Meanwhile, the VQ5 with a mean score of 4.03, which implies the experts affirms and adjudges the BSAM scheme to be inclusive, very comprehensive, and of a very good logical structure. 285 Furthermore, VQ7 was asked to address the content validity for the BSAM scheme, which was integrated within the C-SDSS platform and has a mean score of 4.21. The high mean index of VQ7 indicated that there is a good tendency of successful implementation of sustainability in buildings when there are appropriate use and deployment of the integrated BSAM scheme within the C-SDSS platform. Conclusively, the high mean indexes achieved by the four validation aspects of the validation exercise shows the C-SDSS platform and its integrated BSAM scheme are comprehensive, inclusive, reliable, appropriate, suitable, and applicable for the sustainability assessment of green buildings and to enhance the implementation of sustainability practices in the built environment within the context of the sub-Saharan region of Africa. 9.4 Research implications The current study has developed and demonstrated the deployment and implementation of the C-SDSS platform for the assessment of the sustainability performance of green buildings and to promote the implementation of sustainability practices in building and real estate. The various programming codes, design, and interfaces of the C-SDSS platform have been verified as well as validated using reallife case study building projects and experts’ validation exercises. The developed CSDSS platform has also been implemented in practice in the built environment. The research findings and the deliverables of this study have several beneficial contributions to knowledge and practice as discussed in this section. A key deliverable and contribution of this study are that – the C-SDSS platform and its integrated BSAM scheme green rating system developed in this study provided an automated and dynamic system for decision-makers, assessors, and other relevant stakeholders in the built environment to evaluate the sustainability performance of green buildings. Meanwhile, the C-SDSS platform has illustrated and demonstrated in this study also provides avenues for its users to compare their building designs or models as well as completed building projects for their sustainability potential or performance, respectively. On the C-SDSS platform, the weighting of the buildings’ sustainability indicators and attributes can be compared. Hence, in a scenario where the client or their consultants favor a sustainability criterion, say energy or indoor environmental quality, in their building project; using 286 the “Compare Projects” functionality of the C-SDSS platform – the user or assessor can identify among a set of building designs – the design that best aligns to the intended sustainability criteria targets. More so, the C-SDSS platform when used for the sustainability assessment of green building projects – can help pinpoint areas of the sustainability assessment of a building project or design that needs enhancement in order to meet the client or developers’ pre-set sustainability certification grade or objectives, especially when the calculated BSAR value of the building is below the targeted certification grade level. Besides, the C-SDSS platform is an open-source project (free-to-use) to assess a building project, hence providing a solution to one of the key barriers to the implementation of sustainability and sustainability assessment in the construction industry (Olawumi et al., 2018; Olawumi & Chan, 2020b). Also, the BSAM scheme green rating system which is integrated with the C-SDSS platform provides a suitable regional context rating tool to assess the greenness of buildings within the sub-Saharan region. According to Todd and Geissler (1999) and Banani et al. (2013) the regional or local context has a significant effect on the importance given to sustainability criteria. More so, the C-SDSS platform and its integrated BSAM scheme can serve as a consultative policy toolkit for relevant government departments, national and international organizations, towards enhancing the implementation of green buildings. It also provides for the integration of the United Nations Sustainable Development Goals (SDGs) in the planning and design of buildings and neighborhood developments. The findings and deliverables of this study, as highlighted above have significant benefits to all strata of the society – government, real estate developers and endusers, etc. even the ecosystem – apart from expanding the current body of knowledge. It is expected to improve the quality of life, wellbeing, and the sustainability profile of people, buildings, and the environment within the context of the sub-Saharan region. Also, the findings will be shared and discussed with government agencies, developers, corporate bodies with a view to instigating the formulation of new policies or amendment of the existing ones to that which is more robust and include the three pillars of sustainable development. These deliverables of this study as well as its contributions to knowledge and practice will be measured by keeping track of the users of the C-SDSS platform by 287 getting regular feedback from them as regards various quantifiable and qualitative variables such as user-friendliness, ease of use; and whether the BSAM scheme and C-SDSS platform meet their needs, facilitate or improve their implementation of green building practices among others. 9.5. Conclusions The use of digital technology tools to aid the adoption of green buildings and the sustainability assessment of building projects is essential. More so, for the assessment metric or system to bear significant benefit to its users and the built environment at large, it must embed the three pillars of sustainable development. That is, enhancing social and economic impacts while minimizing environmental impacts. The current study builds on the existing knowledge and bridges the gaps in practice by developing a dynamic cloud-based decision support system (C-SDSS) platform which integrates the BSAM scheme as its primary green building rating system. The BSAM scheme is a recently developed green rating system suited for the sub-Saharan region of Africa and holistically considers the three pillars of sustainable development in its sustainability assessment of buildings. The study utilized high-level programming languages such as PHP, Jscript, etc. as well as relational databases such as the MySQL to develop the C-SDSS platform which is developed with the intent to facilitate the assessment of the sustainability performance of green buildings and infrastructures as well as to promote the implementation of sustainability practices within the context of sub-Saharan region of Africa. More so, the current study improved the sustainability assessment algorithm of the BSAM scheme through the use of the GCFI technique during the coding of the green building sustainability assessment GUI of the C-SDSS platform. The developed C-SDSS platform includes among others, various interfaces for the registration of new green projects on the platform, evaluation of the sustainability performance of buildings, displaying the infographic results of the sustainability assessment, comparing the sustainability credentials of green buildings on a threetier basis. The C-SDSS platform is hosted on a local cloud-based server. The developed C-SDSS platform was validated using four case study building projects – two new building projects and two existing building projects, as well via experts’ opinions. More so, the verification of the C-SDSS platform’s programming 288 codes, design, and interfaces was also undertaken. The verification and validation exercises shield more information and details on the capacity of the C-SDSS platform as discussed in Section 9.3. Meanwhile, the significant benefits, contributions, and implications of the developed C-SDSS platform and research findings to knowledge, practice, and the built environment at large were expatiated upon in Section 9.4. The study recommends the development of digital cloud-based tools such as the CSDSS platform for the other existing green building rating tools such as LEED, BREEAM, BEAM Plus, etc. The study also suggests improvement to these existing rating tools, that is, the inclusion of social and economic sustainability criteria into these rating systems to ensure they wholly consider the three pillars of sustainable development. An apparent limitation of the developed C-SDSS platform is that it is not currently applicable to some regions of the world, other than the sub-Saharan region of Africa, due to its use of the BSAM scheme as its primary green rating system. Future studies can consider integrating more green building rating systems as ‘secondary’ green rating tools within the C-SDSS platform, in addition, to its current primary green building rating tool – the BSAM scheme. More so, further research in this area may consider upgrading the existing C-SDSS platform to automatically process and analyze BIM models to deduce relevant sustainability modelling results such as energy simulations, daylighting consumptions, etc. 9.6 Chapter Summary Digital technological systems are regarded as enablers of the adoption of green buildings and the sustainability assessment of building projects. However, there is currently a dearth of technological tools that can aid the sustainability assessment of buildings. Hence, this study set out to develop a Cloud-based Sustainability Decision Support System (C-SDSS) to facilitate the assessment of the sustainability performance of green buildings. The C-SDSS platform was developed using various high-level programming languages and relational databases. The primary green building rating system used on the C-SDSS platform is the Building Sustainability Assessment Method (BSAM) scheme – which was designed specifically for the subSaharan region of Africa and holistically considered the social, economic, and environmental sustainability criteria. The assessment algorithm of the BSAM scheme 289 was enhanced using an efficient Multi-criteria Decision Making (MCDM) technique before its integration to the C-SDSS. The developed C-SDSS was validated using four real-life building case study projects and via experts’ opinions. The outcomes of the validation exercise show the efficiency of the C-SDSS platform to enable its users to adequately measure and assess the sustainability performance of green buildings. Also, the established C-SDSS platform allows for two or more building projects to be compared to each other based on their sustainability credentials. The study’s findings generate salient benefits and profound impacts to all strata of the society, the ecosystem, and the built environment. The following chapter combines the various deliverables and findings from the previous chapters to develop a holistic Green-BIM Assessment Framework for green building projects in Sub-Saharan Region of Africa. 290 CHAPTER 10: GREEN-BIM ASSESSMENT FRAMEWORK FOR EVALUATING SUSTAINABILITY PERFORMANCE OF BUILDING PROJECTS10 10.1 Chapter Overview The previous chapter developed a C-SDSS platform to facilitate the assessment of the sustainability performance of green buildings. The current chapter combines the various deliverables and findings from the previous chapters to develop a holistic Green-BIM assessment (GBA) framework for green building projects in the SubSaharan Region of Africa. A conceptual research framework approach based on a consolidated desktop literature review form part of the basis of the GBA framework development. The GBA framework is expected to serve as an automated and dynamic digital tool for the evaluation of the sustainability performance of green building projects for comparison and benchmarking purposes. Three of the six components of the GBA framework are based on the deliverables of chapters 7, 8, and 9; hence developed beyond the ‘conceptual’ level. 10.2 Methodology This section discusses the mixed research method employed for the development of the GBA framework. 10.2.1 Conceptual Framework Development The development of research frameworks is essential in the creation of new knowledge (Agherdien, 2007). In the development of research frameworks from the existing literature, there are mainly five steps or algorithms towards it. Although the steps listed below are not the standard linguistic terms, it expresses the purpose or aim of each framework or model development stages. The essential steps of research framework development include: i. The description of the statement of facts, phenomenon, or purpose of the study. This chapter is fully reported in this working paper: Olawumi, T.O., & Chan, D.W.M. Green-BIM Assessment Framework for Evaluating Sustainability Performance of Building Projects: A Case of Nigeria. 10 291 ii. The search for and specifications of the latent concepts, theories, or even existing frameworks that have some connections with the proposed research framework. iii. Review of the underlying concepts and theories; and discussion of the different components or variables of the proposed framework with regard to its positive attributes (e.g., benefits, strengths, drivers, etc.) or negative attributes (e.g., barriers, threats, weaknesses, etc.). iv. The aggregation of the components or variables (main and its subcomponents) of the proposed framework and/or with the underlying theories or concepts that explain each component of the proposed research framework. v. The creation of a diagram or map to interconnect the main components to illustrate how the framework best explains the facts and purpose of the research framework development. There are two types of research frameworks, which are theoretical frameworks and conceptual frameworks (Zack, 2019) and are regarded as paths towards a research inquiry (Dickson et al., 2018). According to Zack (2019), a theoretical framework is a “structure that can hold or support a theory of a research work”; or can be regarded as a research guide or blueprint (Osanloo & Grant, 2016). Fulton and KrainovichMiller (2010) in describing the usefulness of a theoretical framework; compared its roles in a research inquiry to that of a geographical map to a traveler. Meanwhile, Ravitch and Carl (2015) reported that theoretical frameworks help the users to integrate and contextualize latent and formal theories in their research as a guide. It also affects the adopted research design, data collection, and analysis process for such studies (Dickson et al., 2018; Lester, 2005). In summary, a theoretical framework is made of principles, concepts, and constructs that define a theory (Osanloo & Grant, 2016). Moreover, Zack (2019) defined a conceptual framework as “the researcher’s own position on the problem,” and such frameworks might be an adaptation from previous models or frameworks with significant modifications to suit the current inquiry. It is referred to as a structure which presents a natural explanation to a phenomenon being studied (Camp, 2001) and outlined an integrated way and explanation on how best to explore the research problem (Dickson et al., 2018; Liehr & Smith, 1999). It 292 eases the definition and specification of concepts within the research problem (Luse et al., 2012) and shows the series of steps towards achieving the research purpose (Dixon et al., 2001), and the presentation of the key framework variables or constructs in graphical and logical structure to illustrate the relationships between the constructs (Dickson et al., 2018; Miles & Huberman, 1994). The theoretical framework is based entirely on existing theory, while the conceptual framework is the operationalization of such theory. Despite the differences in the concepts and roles of these research framework types in research inquiry, they make research findings relevant, useful, acceptable to the underlying theory or concepts, and allows for the generalizability of the research findings and contributions (Akintoye, 2015; Dickson et al., 2018). Nevertheless, the chosen research framework should resonate with the aim and purpose of the research (Osanloo & Grant, 2016). 10.2.1.1 Why conceptual framework for GBA framework development? The conceptual framework approach was employed to contextualize the proposed GBA framework. The adoption of the conceptual framework development approach was necessitated in this study because: firstly, there are no previous theoretical frameworks or studies which have conceptualized the information exchange workflows of the GBA framework. That is, the extraction of relevant data and statistics from BIM models and simulation software, GBRS, regulatory and policy documents, and other documentary evidence into a relational database; and the use of such building data on a C-SDSS to assess its sustainability performance. Akintoye (2015) argued that when existing theories, concepts, or frameworks are not sufficient or applicable in creating a structure that fulfills the purpose of a study, then it is advisable to utilize the conceptual framework route. More so, secondly, the key variables and concepts of the proposed GBA framework have been will be outlined. Thirdly, these concepts are interconnected to explain their relationships and how they fulfilled the research objective. Lastly, the study aims at the development of a new green-BIM assessment system, which will be useful for organizational firms, government authorities, and other key stakeholders in the built environment. These four reasons corroborated the assertions of Dickson et 293 al. (2018) and Akintoye (2015) and made the conceptual framework approach best suited for the development of the GBA framework in this paper. More so, Akintoye (2015), and Liehr and Smith (1999) posited that a conceptual framework provides the best way that a researcher can generate useful solutions and remedies to his or her defined research problems. It also provides an avenue for the development of a new theoretical construct (Osanloo & Grant, 2016). Fisher (2007) noted that for the development of a ‘good’ conceptual framework, its structure should be illustrated graphically as well as outlining the relations between the constructs. 10.2.2 Experts’ consultation and validation Validation exercise is fundamental to every research study (Lucko & Rojas, 2010), and it is regarded as the significant last lap of the research cycle (Hu et al., 2016a). Validation serves several purposes, such as- (i) ensures the research methodology, and each stage of the research process adheres to the highest quality standard (Lucko & Rojas, 2010). (ii) test whether a developed model achieved an acceptable quality and met defined requirements (Hu et al., 2016a). (iii) ensures the new knowledge emanating from the research meet acceptable credibility from the academic and industry practitioners, government agencies, and other relevant stakeholders; and (iv) enhance the credibility of the research outputs. Moreover, there exists no single definition of validation or concept of validity in the extant literature (El-Diraby & O’Connor, 2004; Kamat & Martínez, 2003). Also, Lucko and Rojas (2010) and Sargent (1991) reported there are no procedures or algorithms to deduce the type of validation technique or statistical method to be employed during a validation exercise; and this poses a lot of challenges to scientists and researchers. Moreover, Law (2007) defined validation as a process to determine whether a proposed model is an accurate and real-world representation of the system towards fulfilling the objective of the study. Yang et al. (2011) stressed further that validation attempts to assess whether the proposed model or framework can predict the performance of the real-life system that it represents. Several approaches to validation have been adopted in the extant literature which include questionnaire surveys, case studies, experiments, modeling and simulation exercises among others (Hu et al., 2016a; Kihn & Ihantola, 2015; Lucko & Rojas, 294 2010; Pyett, 2003; Sousa, 2014; Taherdoost, 2016). More so, other validation approaches used in the literature include the Delphi survey technique (Olawumi et al., 2018; Olawumi & Chan, 2018d; Rajendran & Gambatese, 2009) and observational studies (Leicht et al., 2010). Some studies have adopted a combination of two or more of the highlighted validation methods. However, the validation approach adopted must be suited to the research subject and its knowledge domain – including its research methodology and its results being examined; and according to Fellows and Liu (2008), it should consider the scope and depth of research. Law (2007) argued further that validation and the approaches adopted depends on the objective of the research study. Meanwhile, there are distinctions between validation and verification – while validation deals with “doing the right things,” verification deals with “doing things right” (Lucko & Rojas, 2010; Yang et al., 2011). More so, Yang et al. (2011) attempt to classify the validation techniques into qualitative and quantitative methods. Qualitative validation techniques make use of hypothesis testing of numerical data to examine the relationships among variables (Yang et al., 2011), while the qualitative method involved the use of research techniques that deduce meaning and interpretation of data which are in a construct of words and ideas rather than numerical data (Tuuli, 2009). Validation in quantitative research has to do with the accuracy and reliability of the measurement data, but in qualitative research, the focus is on understanding, explaining, or representing a complex phenomenon (Pyett, 2003). A qualitative validation technique was used in validating the GBA framework using questionnaire surveys. As argued by Yang et al. (2011), a qualitative approach such as the use of surveys provides “valuable, complementary empirical experience, credibility assessment, and improvement recommendations quickly and at low cost.” The feedback from such a survey can also help improve and enhance the research process, including the proposed model, such as the proposed GBA framework. The process of validation is divided into six main areas (Lucko & Rojas, 2010): such as internal validity, external validity, content validity, face validity, construct validity, and criterion validity. Due to the nature of the proposed framework, a qualitative validation approach will be suited in the validation of the proposed green-BIM assessment framework. More so, in designing the validation questionnaire survey, 295 as explained in the subsequent section, four types of validation were found appropriate such as internal, external, construct, and content validity. Internal validity deals with causality, that is, the relations within the data (Leedy & Ormrod, 2001; Lucko & Rojas, 2010). The internal validity of the GBA framework examines whether the proposed framework is easily understandable and easy to use in practice. External validity assesses whether the research results or models are generalizable for prediction purposes (Lucko & Rojas, 2010). For the GBA tool, external validity examines whether the proposed tool is applicable in developing countries within the sub-Saharan region of Africa. Construct validity evaluates whether the theoretical and conceptual constructs of a study or model are appropriate for operationalization. It examines whether the research method, process, and outputs observe and measure what it ought to measure according to the defined objectives (Field, 2009; Leedy & Ormrod, 2001; Lucko & Rojas, 2010). Specifically, it evaluates the comprehensiveness, inclusivity, and appropriateness of the GBA framework; and checks whether the GBA framework is of a logical structure and addresses the research objective. The content validity evaluates whether the content of the study represents a real-world situation or system (Lucko & Rojas, 2010; Taherdoost, 2016). The content validity examines whether the proposed GBA framework is suitable and adequate as a tool for the evaluation of sustainability performance of buildings, and its appropriate use would lead to a successful adoption of BIM and sustainability practices in the built environment. 10.3 Green-BIM Assessment framework: Development and Process Maps This section discusses the development of the various components that made up the proposed GBA system. The proposed GBA framework was developed to serve as an automated and dynamic digital system to aid the sustainability performance of buildings with the support of a cloud-based decision support system. The proposed GBA system (Figure 10.1) has six main components, such as BIM, regulatory documents, data and evidence, the BSAM scheme (a green building rating system), relational databases, and the C-SDSS platform. The proposed GBA system is applicable to building projects and construction organizations where BIM infrastructure is employed, and sustainability practices 296 implemented. More so, the GBA framework is primarily suited to the local context of countries within the sub-Saharan region of Africa. Three of the GBA framework’s components have been fully developed in previous chapters. Hence, this chapter focus on conceptualizing and integrating the other key components (BIM, regulatory documents, data & evidence) towards facilitating the evaluation of the sustainability performance of green buildings. 10.3.1 GBA Framework and its information exchange workflow The development of the GBA framework emanates from the aggregation of its components’ process maps. The GBA framework involves partly the information exchange workflows between its six components and the i/o of data by the system users. Hence, it is necessary to address the “what,” “who,” “when,” and “to whom” of the information workflows to aid the effective adoption and implementation of the GBA framework by project decision-makers and other users of the proposed tool. Table 10.1 presents the “what” aspect of the information exchange workflow of the GBA framework, that is, what needs to be provided in terms of data to be analyzed. Also, Figure 10.2 illustrates the various building development stages of a building project and the various data required. Thus, illustrating the “when” aspect of the information exchange workflow. More so, the “to whom” aspect of the GBA framework’s information exchange workflow defines the expected users of the proposed GBA framework. These include various stakeholders in the built environment such as construction firms, consultants, government agencies, clients, and property developers, professional bodies, among others. Meanwhile, the “who” aspect of the information exchange workflow of the GBA framework refers to practitioners and stakeholders with the required competency to provide the necessary data highlighted in Table 10.1. The “who” aspect has been highlighted within the BSAM scheme documentation (Olawumi & Chan, 2019c) and will not be further discussed in this chapter. 297 Figure 10.1: Components of the proposed GBA tool Note: The thick arrows, such as the arrows indicating the process flow from BIM, regulatory documents, and data & evidence components to the relational databases, and represents the process maps A, B, and C, respectively. Table 10.1: Descriptions of the data/information required for the GBA tool GBA framework’s component BIM system Regulatory Documents Data required Description BM BIM model (contains a coordinated architectural, structural, and building services design, drainage designs). CS Specification for the building projects. MR Modeling results and outputs, e.g., energy simulation, lifecycle assessment, ventilation modeling, flooding & hazard assessment, thermal modeling results, etc. BC Building contracts (or excerpts from the BC) between the client and contractor, and/or other stakeholders. BC should detail the relationship between the client and parties involved, and their roles in the project. PP Project plan schedule for the design and construction stages. It should detail the responsibility matrix, that is, who is responsible for each aspect of the design and construction stages. TPC Data & Evidence Third-party assessment standards and environmental & energy standards, etc.). codes (safety, labor, FS Feasibility study report. It should consider site-wide issues to be addressed during project development. PE Photographic evidence of the buildings’ components, parts, systems, spaces, etc. as required in the C-SDSS documentation. RDI Relevant records, data, and information as it might be required for each stage of assessment, e.g., utility records (logs of energy usage, water, waste, and other utilities), maintenance records, purchase records, surveys, and feedback, and commissioning records, etc. SR Surveys and reports from specialists’ consultants and subcontractors (land surveyors, geologists, ecologists, etc.), test results such as site 298 GBA framework’s component Data required Description investigation, IAQ plan, acoustics, water run-off, flood risk assessment, ecology, heat island effect, risk assessment study reports, etc. Sustainability Assessment Criteria (BSAM) C-SDSS TPAR Third-party assessor reports of the building projects and site to confirm its compliance with the sustainability criteria. TPCC Third-party compliance certificates (e.g., from ISO, environmental organizations, government agencies, or other designated and accreditation bodies). TPI Third-party data and information such as public transportation routes map and timetables, manufacturer and technical manuals, maps, etc. A–H A- Sustainable Construction Practices; B- Site and Ecology; CEnergy. D- Water; E- Material and Waste; F- Transportation; GIndoor; Environmental Quality; H- Building Management. IR Interim review of the sustainability performance of the building design (assess project, view project SER, compare projects [AVC]). IR & C Interim review of the sustainability performance of the building design and certification (AVC). IA An interim assessment of the sustainability performance of the building project and certification (AVC). FA & C A final assessment of the sustainability performance of the building project and certification (AVC). Figure 10.2: Data required by each GBA framework’s component at the building development stages 299 As shown in Figure 10.2, the level of data required by each GBA framework’s component varies from the concept stage to the post-construction stages of a typical building project. 10.3.1.1 BIM system The information, data, and specification of buildings are represented in forms of information models throughout the project development stage (Olawumi et al., 2017; Philipp, 2013). BIM software is a revolutionary information technology (IT) tool used in the design and creation of building models (Ma et al., 2018; Olawumi & Chan, 2019b). Each BIM model (BM) is a structured database containing necessary information about the building elements and parameters (attributes) required at each stage of the building lifecycle. According to Ignatova et al. (2018), the information in the BIM model is provided in the form of either a specification or graphical form. Each specification (CS) in the BIM model contains details such as a “name” and the “technical characteristics” of the element (Ignatova et al., 2018). Each BIM software has two main data transmission formats (Ignatova et al., 2018): the graphical information (using formats such as dxf, dwg, SAT); and the complete set of data- both graphical and numerical (using formats such as RVT, PLA, IFC). Most BIM software programs use the IFC format, and Ignatova et al. (2018) highlighted some problematic issues with the use of the IFC data schema. Moreover, at each stage of the building development and post-occupancy stages, the data stored in the BIM model can be extracted using data schemas such as IFC, gbXML, etc. The extracted data undergoes the required analysis, processing, or simulation; the resultant simulation or modeling result (MR) or output is communicated to the relevant project stakeholders, which forms the basis of a reliable and informed decision making for the building project. As shown in Figure 10.2 and Table 10.1, three kinds of BIM documents may be required from the concept stage of a building project to the post-construction stages. More so, the level of detail (LoD) of the BIM model required for the sustainability assessment of a building varies from a sketch design (BM) during the concept stage to a detail BM at the construction stage, and to as-built BM for the post-construction stages. Also, only the BM is required for the concept stage, while the BM, MR, and the CS is required from the design and planning stage upward; although, the LoD increases as the building project stages progress. 300 Meanwhile, as shown in Figure 10.3, relevant data from the BIM model, such as the BM, MR, and the CS will be extracted and linked with the corresponding sustainability criteria of the BSAM scheme (using programming scripts) that they intend to fulfill. For instance, an energy MR will be linked with indicator C of the BSAM scheme green building rating system (Olawumi & Chan, 2019c) and uploaded to the relational databases of the developed C-SDSS platform to aid the assessment of the sustainability performance of the building. Figure 10.3: Process map for the BIM system of the GBA framework Practitioners can use the GBA system to access the stored BIM model data in the relational database during the process of evaluating the building sustainability performance. Meanwhile, high-level programming languages such as Python, PHP, Jscript, etc. would be suitable to code the scripts required to extract and upload the data. 10.3.1.2 Regulatory Documents The required data that constitute the regulatory documents have been highlighted in Table 10.1 and outlined in the BSAM scheme documentation (Olawumi & Chan, 2019c). As shown in Figure 10.2, three kinds of regulatory documents may be required from the concept stage to the post-construction stages. Also, only the BC and TPC documents are necessary for the concept stage and the post-construction 301 stages, while the BC, TPC, and PP documents are required for only the design and planning stage and construction stage. More so, to allow the various regulatory documents (BC, TPC, and the PP documents) to be adequately considered when evaluating the sustainability performance of building projects using the GBA framework. The three regulatory documents must be available in either hardcopy or softcopy document format (preferable). As shown in Figure 10.4, excerpts from these documents are extracted and linked with the BSAM scheme using a script. The resultant linked data are uploaded to the relational database of the GBA system using another script. The uploaded data in the relational database (say, MySQL database) is then accessible to the relevant green building assessors using the C-SDSS platform to assess the sustainability performance of green buildings. Figure 10.4: Process map for the Regulatory documents of the GBA framework 10.3.1.3 Data and Evidence The required information that embodies the data and evidence component has been highlighted in Table 10.1. As shown in Figure 10.2, seven kinds of data and evidence documents may be required from the concept stage to the post-construction stages. More so, the LoD of the SR data needed for the assessment of the sustainability performance of the building project varies from a partial SR during the design and 302 planning stage to a detailed SR from the construction stage upward. At the concept stage, only the FS data is needed. In contrast, at the design and planning stage, in addition to the FS data, the SR, TPAR, TPCC data are required for the evaluation of the sustainability performance of buildings. Meanwhile, from the construction stage upwards, the SR, TPAR, TPCC, TPI, PE, and RDI data are required for similar assessments. More so, to allow for the various data and evidence documents to be considered when evaluating the sustainability performance of green building projects using the GBA framework; some of the data and evidence documents (such as the FS, RDI, SR, and the TPAR) must be available in either hardcopy or softcopy document format (preferable). The other three sets of data and evidence documents (TPI, TPCC, and the PE) should be made available in softcopy format – either in an image file format such as JPEG, TIFF, PNG, or as a PDF document. As illustrated in Figure 10.5, excerpts from these documents are extracted and linked with the BSAM scheme using programming scripts – establishing a link between the regulatory documents and the BSAM scheme green rating system. The resultant linked data are uploaded to the relational database of the GBA tool using another script. The data in the relational database is then accessible for the relevant assessor via the C-SDSS platform during the evaluation of the sustainability performance of the green building projects. Figure 10.5: Process map for the Data and Evidence component 303 10.3.1.4 BSAM scheme green building rating system The BSAM scheme developed for the sub-Saharan region is well outlined in the BSAM scheme documentation (Olawumi & Chan, 2019c). The BSAM scheme was integrated with the GBA framework, as its primary green building rating system. The BSAM scheme comprises of three hierarchical levels of sustainability criteria – indicators, attributes, and sub-attributes – which can be linked with the GBA framework’s components A, B, and C to aid the sustainability assessment of green buildings. As shown in Table 10.1, the BSAM scheme green building rating system comprises of eight sustainability indicators (criteria) (Olawumi & Chan, 2019c). As shown in Figure 10.2, the sustainability criteria which are assessed in green buildings vary from the concept stage of such buildings to the post-construction stages. For instance, during the concept stage, only sustainability criteria A, B, C, and G of the BSAM scheme are assessed during the evaluation of the sustainability performance of the building project. Others include, for the design and planning stage (criteria A – G), post-construction stages (criteria B – H), sustainability criteria A – H are assessed at the construction stage. 10.3.1.5 Relational databases and C-SDSS platform The C-SDSS is a cloud-based digital system that functions as a decision support system for the GBA framework, where relevant stakeholders and green building assessors can assess the sustainability performance of green building projects. The relational databases and the developed C-SDSS system are hosted on a cloudbased server and are designed to operate together seamlessly. The C-SDSS platform was developed mainly using the PHP high-level programming language as well as Jscript, while an open-source relational database management system – MySQL was used to manage the data on the GBA system. The C-SDSS platform has several vital interfaces such as – (i) two interfaces to register the details of the green building assessor and the building projects; (ii) the green building project assessment interface (see Figure 10.6); (iii) an interface to view each project assessment scores; (iv) an interface to compare the sustainability assessment scores of building projects; and (v) an interface to view all building projects registered by the assessor on the C-SDSS platform. 304 Figure 10.6: The C-SDSS Project Assessment Interface As shown in Figure 10.2, the type of sustainability assessment for green building projects (and the certification regimes) that can be undertaken on the GBA system varies across the building development stages. At the concept stage, an interim review of the sustainability performance of the building project is carried out since only the sketch design of the BIM model is available, while the IR and certification are deferred to the design and planning stage when the detailed BM design is available. An interim assessment is undertaken at the construction stage, while the final assessment and certification are undertaken at the post-construction stages. 10.4 Experts’ validation surveys This section presents the results of the expert validation surveys carried out to validate the suitability, credibility, and quality of the proposed GBA framework as a tool to aid the assessment of the sustainability performance of green buildings, as well as promote the implementation of green-BIM in the built environment. Two modes of survey distributions were adopted – the fill-in PDF survey forms; and online survey forms (Olawumi & Chan, 2019a). The fill-in PDF survey form was sent as an email attachment to the targeted respondents, along with the link to the online survey form. Andrews et al. (2003) and Olawumi and Chan (2019a) highlighted some benefits these distribution modes to include time and cost savings as well as the ease to communicate and get feedback from the survey participants. 305 The validation exercise comprises of four sections. The first section of the survey form solicited relevant background information from the respondents, while the second section presented the process maps of the six GBA framework’s components and how it relates to the building lifecycle stages. The third section presented the proposed GBA framework, while the fourth section presented the eight validation questions which relate to the four validity types – internal, external, construct, and content validity; and solicited for the perception and opinions of the survey participants. The experts were requested to rate their level of agreement on each of the eight validation questions using a 5-point Likert scale (1 = strongly disagree; 3 = Neutral, and 5 = strongly agree) (Olawumi & Chan, 2019d). 10.4.1 Experts’ demographics The selection criteria used to invite the survey respondents include those with adequate experience in BIM and green building adoption implementation in Nigeria as well those with requisite expertise in the built environment. Purposive sampling technique was used to for the survey form distributions. Based on the above, potential experts were identified and sent emails with links to the online survey and the PDF fill-in survey. Overall, 25 responses were received, of which 20 responses were valid for further analysis. Of the 20 valid responses, 19 responses were collated via the online survey form, and only one response was collected via the PDF fill-in survey form. The sample size for this study is adequate for the validation questionnaire survey when compared with previous studies such as Darko (2018), Osei-Kyei (2018), and Ameyaw (2014), which utilized 5, 6, and 7 respondents respectively to validate the proposed models/tools. Table 10.2 shows the demographics of the invited experts. The experts were asked to indicate their position within their organizations, and they were divided into the three major levels of management – top-level, middle-level, and first-level management (Jones & George, 2006). 306 Table 10.2: Demographics of the experts involved in the validation process Description Frequency Percentage Description Frequency (%) Major profession or Positions occupation Architects 4 20 Top-level 9 managers Civil Engineers 1 5 Middle-level 8 staff Project Managers 2 10 First-level staff 3 Quantity Surveyors 7 35 Total 20 Estate valuers 2 10 Builders 1 5 Academics 3 15 Total 20 100 a Average years 15 5 2.5 Note: aAverage years – The average years of experience (of the respective expert’s management level) in the construction industry. The analysis of expert demographics reveals the experts are from diverse groups of key stakeholders involved in the construction industry and possess adequate years of experience in the subject matter. More so, more than two-thirds of the experts are either in a top-level managerial position or in middle-level roles, which shows the invited experts have the requisite experience in the built environment. The analysis of the demographics of the invited experts further lends credence and reliability to the data collected. 10.4.2 Validation survey results Table 10.3 shows the analysis of the level of agreement of the invited experts to the eight validity statements. It is worthy to note that some of the experts were consulted during the development of the GBA framework, who proffered modifications to some aspects of the proposed GBA framework before its eventual development. Five of the validity statements (VS) have a mean value of at least 4.00 and the remaining three statements – VS3, VS4, and VS8 have mean values of 3.85, 3.90, and 3.95 respectively which is classified as “very important” based on Li et al. (2013) factors’ classification scheme. Hence, the analysis of the experts’ perception implies that the four validation aspects – internal, external, construct, and content validity – for the GBA framework is adequate. The validity statements relating to external validity are VS1 and VS8, while VS4 and VS6 addressed the internal validity. Moreover, VS2, VS3, and VS5 measure the construct validity, and VS7 relates to content validity. 307 The two validation statements (VS1 and VS 8) that relate to external validity have an average mean of 4.05. VS1 obtained a mean index of 4.15, which implies the proposed GBA framework’s components and its process maps adopted to achieve a holistic evaluation of the sustainability performance of buildings, within the context of sub-Saharan Africa, is very reasonable. Besides, VS6, with a mean score of 3.95, confirms the suitability and adequacy of the GBA framework to act as a tool for the assessment of the sustainability performance of buildings is high. As regards the internal validation statements (VS4 and VS6), the average mean score is 4.03. VS4 has a mean index of 3.90, which indicated the proposed GBA tool and the process maps of its components are easily understandable to its users and can be effectively deployed for use in the built environment for green building assessment. Besides, VS6, with a mean score of 4.15, confirms that the development of the proposed GBA framework has sufficiently addressed the objective of this study. In evaluating the construct validity of the GBA framework, three validation questions (VS2, VS3, and VS5) were asked and resulted in an average mean of 3.983. VS2, with a mean score of 4.05, affirmed that the required documents outlined for each component of the GBA framework and its associated process map are very adequate and appropriate. Also, VS3 with a mean index of 3.85, certified that the information required from each of the GBA framework’s component to assess the building sustainability performance at each building lifecycle stage are very adequate and appropriate within the context of sub-Saharan Africa. More so, VS5 had a mean score of 4.05, which signifies and adjudges the proposed GBA framework to be inclusive, very comprehensive, and of a very good logical structure. Meanwhile, the content validity for the proposed GBA framework development was measured using VS7 and had a mean index of 4.25. The mean value for the VS7 indicated that there is a very high tendency of a successful implementation of smart and sustainability practices in buildings, when and if, relevant stakeholders in the built environment adequately adoption, deploy, and make use of the proposed GBA framework. In summary, the high mean scores attained by the four validation facets of the GBA framework show that it is credible, reliable, replicable, comprehensive, appropriate, inclusive, and suitable for the promotion of smart sustainable practices in the built environment as well as the assessment of the sustainability performance of green buildings within the context of the sub-Saharan region of Africa. 308 Table 10.3: Validation results of the GBA framework Level of agreement (%) Code VS1 Validation statement/questions The identified GBA framework’s Mean Strongly agree Agree Neutral Disagree Strongly disagree 20 75 5 - - 4.15 15 75 10 - - 4.05 10 65 25 - - 3.85 25 50 15 10 - 3.90 15 75 10 - - 4.05 20 75 5 - - 4.15 35 55 10 - - 4.25 15 65 20 - - 3.95 components and its process maps adopted to achieve a holistic evaluation of the sustainability performance of buildings in reasonable. VS2 The required documents within each GBA framework’s component and its process map are adequate and appropriate. VS3 The information required from each GBA framework’s sufficient to sustainability component assess a performance is building at each building lifecycle stage are adequate and appropriate. VS4 The developed GBA framework and its components’ process maps are easily understandable and easy to use in practice. VS5 The developed GBA framework is inclusive, comprehensive, and of a logical structure. VS6 The development of the GBA framework sufficiently addresses the objective of the study. VS7 The appropriate adoption and use of the GBA framework as a tool would lead to a successful implementation of smart and sustainability practices in buildings. VS8 The GBA framework as a tool is suitable and adequate to assess the sustainability performance of buildings. Note: VS – Validation statement 309 10.5 Research implications and recommendations The GBA framework and its associated process maps will enhance the capacity of stakeholders to support the implementation of smart sustainable practices in the Nigeria construction sector. Also, industry practitioners can deploy the GBA system to assist in the evaluation of the sustainability performance of green buildings in Nigeria. Its implementation by relevant stakeholders will also enhance the adoption of the green-BIM initiatives in Nigeria. The GBA system can also be used to compare the sustainability performance of different construction projects and building for benchmarking purposes. As argued by Yuan et al. (2019), project owners and other associated project team members are vital to the adoption and implementation of BIM and sustainability in the construction industry and play salient roles in its diffusion within the built environment. Hence, it is recommended for stakeholders within the sub-Saharan region to embrace and adopt the GBA framework. More so, Martin et al. (2018) and Lim et al. (2018) argued for the government and private sectors to get involved in advocacy roles to drive the digitalization of the built environment and enhance the implementation of smart and sustainability initiatives. Within the context of the subSaharan region where there is a low-level adoption of green-BIM initiative and practices (Jung & Lee, 2015; Olawumi et al., 2017; Olawumi & Chan, 2017, 2018a), such advocacy is needed from every stakeholder involved in the built environment. Necessary strategies are recommended for implementation by construction stakeholders to ensure the GBA framework adequately facilitates the adoption and implementation of green-BIM initiatives. These strategies are dissemination, development, and advancement. Firstly, by dissemination – the study findings, as well as its key deliverables – such as the developed GBA framework and its process maps should be shared via various forums and conferences where relevant stakeholders in the built environment are involved. Also, local meetings organized by industry practitioners, professional institutions, and green building councils, among others – such as the West Africa Built Environment Research (WABER) Conference, is another avenue of demonstrating the GBA system and usefulness to evaluate the sustainability performance of buildings. 310 More so, by development, further practical and real-life case study validation of the developed GBA framework should be undertaken sustainability assessment of green building by conducting projects for real-life comparison and benchmarking purposes. More so, top management of construction firms should be encouraged and supported to create their in-house customized versions of the GBA system. Lastly, by advancement – comprehensive guidelines and practice notes for implementing smart sustainable practices should be formulated for reference purposes. It will provide practitioners with the necessary best practices and guidelines for deploying the GBA framework in the construction industry. Overall, the implementation of the GBA framework will help increase the capacity of construction professionals and organizations to enhance the sustainability potential of their green building projects in the future. 10.6 Conclusions The interest in sustainable buildings and cities as well as smart buildings as gained traction in recent years due to the various socio-economic benefits derivable from its adoption. Recently, there have been calls to consolidate the initiatives of green and smart buildings for implementation within the construction industry. However, a plethora of barriers had hindered these initiatives. Hence, this study proposed and developed a GBA framework that integrates green and smart components such as BIM, BSAM scheme green building rating system, regulatory documents, data and evidence, relational databases, and the C-SDSS digital platform. The established GBA framework provides a cloud-based automated system for the holistic assessment of the sustainability performance of green buildings. The developed GBA tool comprises six distinct components and builds on the previous research, which are products of larger project work. This study outlined the 4Ws of the information exchange workflows that can help practitioners to understand the operationalization of the GBA framework towards facilitating its implementation within the sub-Saharan region. The study discussed the components of the GBA framework and how it functions to aid the assessment of the sustainability performance of buildings. More so, the developed GBA framework was validated by experts, and the analysis of the validation results provided credence to the applicability of the GBA framework 311 to help in the digitalizing the sustainability assessment of green buildings and enhance the implementation of smart sustainable buildings in the region. The practical implication of the adoption of the GBA framework in the construction industry was highlighted, as well as recommendations and strategies to enhance its implementation. A limitation of the study is that the GBA framework development focused on the context of the Nigeria construction industry, although it could be applied to other countries in sub-Saharan Africa. Future research studies can focus on validating the GBA framework in real-world building projects. More so, further researches can work on embedding more technological tools along with BIM within the GBA framework. Meanwhile, to allow for the applicability and generalizability of the proposed GBA framework beyond Nigeria and the sub-Saharan region, future studies can extend the GBA framework to incorporate other GBRS such as LEED, BREEAM, and CASBEE, among others. 10.7 Chapter Summary The advantages of the use of technology-enhanced tools to aid the sustainability assessment of green buildings is well established in the literature, and this informs the development of the green-BIM assessment (GBA) framework in this chapter. Hence, a mixed research design was adopted to formulate and establish the different components of the GBA framework. The GBA framework incorporates the BSAM scheme as the primary green building rating system in the proposed framework. The various components of the GBA framework were discussed and how its components’ functionalities aggregate to aid the assessment of the sustainability performance of buildings. Practitioners adopting the developed GBA system – an automated and dynamic digital tool – will assist them to facilitate the implementation of smart sustainable practices in building projects. Also, the proposed GBA framework was validated which highlights its suitability and applicability in the construction industry. The GBA framework will also enable practitioners to compare the sustainability performance of building projects for benchmarking purposes. The next chapter of this thesis concludes the research. 312 CHAPTER 11: CONCLUSIONS AND RECOMMENDATIONS 11.1 Chapter Overview The previous chapters have provided an overview of the study and employed several research methodologies to provide answers to the research objectives towards achieving the study’s aim. The current chapter concludes the research study. The research aims and objectives are reviewed; and its conclusions highlighted. The significance, value, and contributions of the study to knowledge and practice are buttressed. More so, the limitations of the study are outlined with relevant recommendations for future studies. The conclusions, significance and contribution of the research findings, limitations of the study, and recommendations for future studies highlighted in this Chapter 11 have already been thoroughly discussed in the previous chapters. Hence, a summary of these are presented here. 11.2 Review of the Research Objectives and Conclusions This study aims to develop a green-BIM assessment model and cloud-based sustainability decision support system for evaluating buildings' compliance to sustainability principles with a view to integrating smart sustainable practices in building construction and management, improving operational efficiency, and enhancing the overall implementation of sustainable development in the built environment. The scope of study mainly focuses on those developing countries located in the sub-Saharan region of Africa with practical applications to other regions. The following research objectives were pursued and established to achieve the study’s aim: 1. To identify and assess the inherent benefits, barriers and critical success factors (drivers) associated with integrating BIM and sustainability principles in building projects. 2. To establish the relative weightings of the key sustainability indicators, sustainability attributes and sub-attributes for buildings. 313 3. To develop a sustainability evaluation index for buildings using the Generalized Choquet Fuzzy Integral method. 4. To develop a cloud-based sustainability decision support system (C-SDSS) for buildings. 5. To develop a conceptual Green-BIM assessment framework as a tool for the evaluation of sustainability performance of buildings. A range of research approaches were adopted towards achieving the above objectives as discussed in Chapters #1, #4 – #10. More so, the principal findings and implications of the study as well as the conclusions which have been discussed and presented in previous chapters (Chapter #4 – #10), are summarized in this section through the review of each of the objectives. 11.2.1 To identify and assess the inherent benefits, barriers, and critical success factors (drivers) associated with integrating BIM and sustainability principles in building projects. A review of the extant literature and practice formed the bedrock for gathering the 36 benefits, 38 barriers, and 30 drivers’ factors of the implementation of BIM and sustainability (smart sustainable practices) in the built environment. The factors formed the questionnaire items sent to the survey participants. A total of 220 respondents from 21 countries participated in the empirical questionnaire survey, which constitutes professionals of varied backgrounds and from different organization setups; and have direct and extensive experience in smart sustainable practices. The study meanwhile conducted a comparative assessment of the perceptions of the study participants based on their professional disciplines and organizational backgrounds towards establishing patterns of difference. The diversity of the respondents, their levels of experience, and the sample size lend further credence to the data collected. The review of extant literature also revealed the deep-seated variance in the adoption, trend, and application of BIM and sustainability practices; and noted the shortcoming of the existing green rating tools that place more consideration on environmental sustainability. Firstly, for the benefit of BIM and sustainability implementation. Most of the respondents’ groups agreed on the benefit factor “enhance overall project quality, productivity, and efficiency” as the most significant benefit of BIM and sustainability 314 practices implementation in construction projects. Other key factors are (1) real-time sustainable design and analysis early in the design phase. (2) facilitate sharing, exchange, and management of project information and data. (3) Better design products and facilitate multi-design alternatives; and (4) prevent and reduce materials wastage through reuse and recycling and ensure materials efficiency. A factor analysis of the thirty-six benefit factors using the PCA method resulted in five underlying clusters. Secondly, for the barriers to the implementation of BIM and sustainability in the built environment; a significant finding of this study is that there is a relative level of agreement among most of the groups of respondents on the barrier factor “inadequacy of requisite experience, knowledge, and skills from the workforce” as a critical impediment to the implementation of smart sustainable practices in the built environment. The other critical barriers or impediments to smart sustainable practices implementation are (1) Industry’s resistance to change from traditional working practices. (2) Longer time in adapting to new technologies (steep learning curve). (3) Lack of understanding of the processes and workflows for BIM and sustainability; and (4) The high initial investment in staff training costs. Another profound research finding is the classification of the critical barriers or impediments via factor analysis of the 38 barrier factors yielded 7 clusters. Thirdly, for the drivers of BIM and sustainability implementation. There was a consensus among the various respondents’ groups on the need to organize training programs and workshops for the training of cross-field specialists who are skilled and knowledgeable about smart technologies and sustainability issues. Other significant drivers are the (1) Technical competence of staff. (2) Early involvement of project teams. (3) Greater awareness and experience level within the firm; and (4) Effective collaboration and coordination among project participants. A factor analysis of the perceived individual drivers of smart sustainable practices yielded five-factor clusters. Based on the review of extant literature and the perceptions of the respondents, it is revealed that the construction industry still lags in the adoption and implementation of smart and sustainable practices in the construction industry. It is evident from these significant research findings and collective perspectives that the implementation of BIM and sustainability practices have an essential role to play as 315 well as exerts profound impacts on construction projects and the built environment. Also, there must be synergy among all relevant construction stakeholders, firms, and government agencies towards achieving smart sustainable practices in the built environment. Collaborative efforts from policymakers, local authorities, practitioners, academics, and other key stakeholders can help to combat these challenges. It is envisaged that the research findings have stimulated multitudinous open debate for reference to the underlying problems besetting the built environment in each local context and internationally. 11.2.2 To establish the relative weightings of the key sustainability indicators, sustainability attributes and sub-attributes for buildings. A review of the extant literature, existing green rating systems, green building technical notes, and guidelines using the content analysis approach as well as experts’ consultation helped in identifying the sustainability criteria – environmental, social, and economic sustainability criteria – of the BSAM scheme. The need for green building rating systems to focus on the three pillars was also emphasized. Eight (8) key sustainability criteria were identified for inclusion in the BSAM scheme. The link to the publicly available repository of the 77-page BSAM scheme documentation was provided in Chapter 7 (section 7.3.2). In determining the credit weighting of each of the BSAM scheme sustainability criteria – experts consultation exercises were conducted – via the use of questionnaire survey forms, informal discussions, and interviews – of which well experienced and diverse set of experts participated in the development of the BSAM scheme. The weights and the significance of each sustainability sub-attributes were established; also, the criteria-based ranking of the BSAM scheme based on its attributes and sub-attributes was generated. The BSAM scheme certification grading system scales were established which are outstanding, excellent, very good, good, acceptable, and unclassified, which are on the scale of 0-100%. Two case studies of building projects (residential and commercial buildings) were employed to validate the BSAM scheme. Further validation exercise was done by means of a comparative analysis of the BSAM scheme with six selected common green rating tools – LEED, BEAM Plus, BREEAM, IGBC, Green Mark, and Green Star, as discussed in Chapter 7. Based on the comparison of these green rating tools, several conclusions were reached, as highlighted in Chapter 7. The conventional “aggregation of points” 316 technique used in Chapter 7 limits the expressions of the key criteria and fails to address the complex relationship among the key sustainability criteria of the BSAM scheme. These two limitations are addressed in chapter 8 (Objective #3). 11.2.3 To develop a sustainability evaluation index for buildings using the Generalized Choquet Fuzzy Integral method. The current study used an MCDM technique, the generalized Choquet fuzzy integral (GCFI) method using the TFN to develop a building sustainability evaluation index (BSEI) for the BSAM scheme. The GCFI method adopted in Chapter 8 helped addressed the limitations of the conventional “aggregation of points” technique used in Chapter 7. The data collected from the 189 invited experts were analyzed using the GCFI algorithm. The resulting sustainability index and building classification system was used to assess the sustainability performance of four real-world case studies of building projects. Also, the advantages and superiority of the GCFI over other weighting techniques were discussed in the study. The use of the GCFI also helped addressed the shortcomings in these existing green rating tools, which only utilize points aggregation which have been reported in the literature and discussed in Chapter 8 to be an insufficient metric. As it is revealed in the weighting calculation for the respective eight sustainability criteria of the BSAM scheme, significant priority was given to criteria such as sustainable construction practices, transportation, and energy with normalized weights of 0.1346, 0.1286, and 0.1271 respectively. More so, the sustainable construction practices criteria contain a considerable proportion of the social and economic sustainability criteria which were not considered in the existing green building rating tools. The practical contributions of Objective #3 from the industry and theoretical standpoints are also discussed in Chapter 8. The BSAM scheme can assess the sustainability performance of both new and existing buildings. 11.2.4 To develop a cloud-based sustainability decision support system (CSDSS) for buildings. The current study builds on the existing knowledge and bridges the gaps in practice by developing a dynamic cloud-based decision support system (C-SDSS) platform which integrates the BSAM scheme developed in Chapters 7 and 8 as its primary green building rating system. The study utilized high-level programming languages such as PHP, Jscript, etc. as well as relational databases such as MySQL to develop 317 the C-SDSS platform. The C-SDSS platform was developed with the intent to facilitate the assessment of the sustainability performance of green buildings and infrastructures within the context of the sub-Saharan region of Africa. The developed C-SDSS platform includes, among others, various interfaces for the registration of new green projects on the platform, evaluation of the sustainability performance of buildings, displaying the infographic results of the sustainability assessment, comparing the sustainability credentials of green buildings on a three-tier basis. The C-SDSS platform is hosted on a local cloud-based server. The developed C-SDSS platform was validated using four case study buildings as well via experts’ opinions. More so, the verification of the C-SDSS platform’s programming codes, design, and interfaces was also undertaken. The verification and validation exercises shield more information and details on the capacity of the CSDSS platform as discussed in Chapter 9 (section 9.3). Meanwhile, the significant benefits, contributions, and implications of the developed C-SDSS platform and research findings to knowledge, practice, and the built environment at large were expatiated upon in Chapter 9. The study recommends the development of digital cloud-based tools such as the C-SDSS platform for the other existing green building rating tools such as LEED, BREEAM, BEAM Plus, etc. The study also suggests improvement to these existing rating tools, that is, the inclusion of social and economic sustainability criteria into these rating systems to ensure they wholly consider the three pillars of sustainable development. 11.2.5 To develop a conceptual Green-BIM assessment framework as a tool for the evaluation of sustainability performance of buildings. Objective #5 developed a conceptual green-BIM assessment (GBA) framework which integrates a few green and smart components. These components include BIM, a regional-based green building rating system (BSAM scheme), regulatory documents, data and evidence, relational databases, and a C-SDSS digital platform. The GBA framework has provided a cloud-based automated system for the holistic assessment of the sustainability performance of green building and infrastructure projects. Before the development of the conceptual GBA framework, an extensive review of the extant literature led to the formulation of five critical steps essential for the development of any research frameworks – whether it is a theoretical or conceptual framework. The developed GBA framework consists of six main 318 components as mentioned earlier, and three critical process maps, namely BIM, regulatory documents, and data and evidence process maps. Moreover, the study provided detailed descriptions of the key components of the GBA framework and how they are to be used to achieve a smart sustainable building as well as the associated assessment of green buildings. The level of detail (LOD) required for each set of documents in each GBA framework’s component was illustrated. It is worthy of note that three of the six GBA framework have been developed, as discussed in Chapters 7, 8, and 9. Furthermore, the developed GBA framework was validated. The invited experts graded the proposed GBA framework based on eight validation statements or questions which covered content, construct, internal, and external validations. The proposed GBA framework will provide a sound platform for evaluating the sustainability performance of green building projects towards as well as aiding effective smart sustainable decision-making process in building projects with a broad range of benefits towards promoting sustainable development in the built environment. 11.3 Significance and Contributions of the Study The research study contributes to existing knowledge and practice by providing the salient and key factors – benefits, barriers, and drivers – that influence and facilitates the implementation of BIM and sustainability practices in the built environment. Also, the ranking of these key factors can form a sound basis for the development of an efficient and well-informed decision-making process by government departments, agencies, and other key construction stakeholders. The findings from the surveys of Objective #1 can also be adopted as a policy instrument and useful guidelines for government agencies, stakeholders, and others towards ensuring BIM can be used to deliver the full potential of sustainability practices in the construction industry. More so, it provides effective strategies and recommendations towards increasing the uptake of BIM and sustainability practices and enhancing the full implementation of smart sustainable practices in the construction industry. A summary of these recommendations includes that – (1) Local authorities and government departments should liaise with relevant built environment professional bodies to set-up ‘green-BIM compliance’ incentives to motivate construction firms and clients to implement the concepts in their projects. They should also work synchronously towards developing 319 relevant policies and standards to aid the adoption of these concepts within the local context. (2) Key stakeholders in the construction industry are encouraged to prioritize early collaboration and coordination of their activities at the early stages of project development. (3) For countries that are yet to develop their BIM and sustainability assessment standards, the establishment of such standards is advocated, as this will provide both qualitative and quantitative guidelines to assess the impact of green BIM on the built environment. Other recommendations include (4) Construction projects are encouraged to develop green BIM execution plan for use in construction projects as well as pursue the early adoption of green BIM initiatives at the planning stage of project development. (5) Professional bodies and construction firms should engage more in the training of their members and staff through the mediums of training workshops and knowledge seminars; (6) Increase in funding support (both in research and practice) from the government and corporate bodies to aid the adoption of smart sustainable practices. (7) Provision of government subsidy to ease the ‘financial stress’ of small and medium scale construction firms. (8) It advised that colleges and universities enrich their curriculum based on the recent trends in BIM and sustainability practices. (9) The need for construction firms and stakeholders to be proactive in adopting new and innovative concepts. (10) The development of open-source or affordable cloudbased technologies should be accelerated to mitigate against the potential barrier posed by the cost of purchasing the commercial desktop-based software. More so, the development of the Building Sustainability Assessment Method (BSAM) scheme to suit the regional context of the sub-Saharan region of Africa is a significant contribution to knowledge and practice. It is the first attempt at developing a holistic green building rating system for countries within Africa in a local context. The BSAM scheme encompasses the necessary sustainability criteria as well as an improvement on the existing green rating tools. The study also utilized a robust MCDM technique – the GCFI method to determine the weightings of the BSAM scheme to evaluate the sustainability performance of building projects. The BSAM scheme is available to assess both new and existing buildings. The proposed BSAM scheme also provided practical guidelines towards evaluating green buildings as well as the documentary evidence to be assessed and verified to ascertain the fulfillment of the sustainability criteria. It also covers the maintenance 320 and improvement of the sustainability performance of the buildings throughout their lifecycles. Moreover, implementing the proposed BSAM scheme can promote greener buildings and sustainable development and guide the design team as well as the construction team to employ greener technologies to achieved priority sustainability criteria. It also helps fulfills the need for a technical scheme through the experience-based ranking of the key criteria. The BSAM scheme also enables the comparative assessment of buildings using unified sustainability criteria and evaluation model based on the BSAM scheme. It helps pinpoint aspects in the sustainability performance of buildings that need more improvement based on project’ objectives. It is recommended for each developing country in the sub-Saharan region to establish its green building councils towards joining the global body. Also, stakeholders in the built environment are encouraged to adopt and test the proposed BSAM scheme in evaluating their building projects to accelerate the implementation of this green rating tool. A key deliverable and contribution of the development of the C-SDSS platform and its deployment in this research study is that – the C-SDSS platform and its integrated BSAM scheme green rating system provided an automated and dynamic system for decision-makers, assessors, and other relevant stakeholders in the built environment to evaluate the sustainability performance of green buildings. It also provides a digital avenue for users to compare their building designs or models for their sustainability potential or performance respectively. Besides, the C-SDSS platform being an open-source project (free-to-use), provides a cost-effective tool and solution in the assessment of building sustainability performance. More so, the C-SDSS platform and its integrated BSAM scheme can serve as a consultative policy toolkit for relevant government departments, national and international organizations, towards enhancing the implementation of green buildings. It also provides for the integration of the United Nations Sustainable Development Goals (SDGs) in the planning and design of buildings and neighborhood developments. It is expected to have significant benefits to all strata of the society – government, real estate developers and end-users, etc. even the ecosystem – apart from expanding the current body of knowledge. It is expected to improve the quality of life, wellbeing, and the sustainability profile of people, buildings, and the environment within the context of the sub-Saharan region. 321 Moreover, the developed GBA framework provides a cloud-based automated system for the holistic assessment of the sustainability performance of green building and infrastructure projects. The developed GBA framework is also able to enhance the implementation of smart sustainable practices and enable practitioners to compare the sustainability performance of building projects for benchmarking purposes. 11.4 Limitations and Recommendations for Future Research Study In achieving Objective #1 using empirical questionnaire surveys to collate data from international experts, there is a limitation in the relatively small sample size for regions such as Europe (30) and America (12), both in the number of respondents and countries. However, the level of experience of the respondents from these two regions helps to minimize this limitation. Future studies can conduct in-depth surveys on these regions as well as projects in these countries for comparisons. Also, an obvious limitation of this study is that only BIM out of the several smart technological tools was examined as it influenced sustainability practices. The justification for this has been provided in Chapter 1, sections 1.3.2 and 1.3.6. It is recommended that future research studies should explore and conduct a quantitative cost-benefit analysis of the gains of green-BIM implementation in the construction industry to provide a sound basis for project comparison and benchmarking. It is also recommended for future studies to examine the key benefits, barriers and drivers highlighted in this study based on an in-depth case study of construction projects, organizations, and countries and ways of maximizing each stakeholder input towards extending the scope of the current research and substantiating the critical findings derived in the study. This kind of future studies can help verify and evaluate the feasibility and effectiveness of those identified key factors in promoting and achieving smart sustainable practices in the built environment Future research studies can focus on expanding the scope of the key sustainability criteria of the BSAM scheme and adding more variables at each sub-level – attributes, and sub-attributes. A limitation of this study is that the developed BSER value and the sustainability evaluation model are based on the BSAM scheme, which is region-specific. However, the GCFI algorithm can be applied to the other green rating tools to develop their BSER – although these tools focused heavily on 322 only the environmental sustainability construct. For this reason, it is recommended for the future development of these existing green rating tools and other regional tools to incorporate the social and economic aspects of sustainable development. For future studies, the GCFI can be applied to other green tools or to evaluate various sustainability issues in the built environment. More so, countries using the existing green rating systems such as LEED, BREEAM, BEAM Plus, etc. which emphasizes the environmental sustainability are implored to examine the social and economic sustainability criteria in the BSAM scheme with a view to including in their country or region customizable green rating system towards improving it. An apparent limitation of the developed C-SDSS platform is that it is not currently applicable to some regions of the world, other than the sub-Saharan region of Africa, due to its use of the BSAM scheme as its primary green rating system. Future studies can consider integrating more green building rating systems as ‘secondary’ green rating tools within the C-SDSS platform. More so, further research in this area may consider upgrading the existing C-SDSS platform to automatically process and analyze BIM models to deduce relevant sustainability modeling results such as energy simulations, daylighting consumptions, etc. A limitation of the development of the GBA framework is the integration of only BIM and the native C-SDSS digital platform as the smart tools in the GBA framework. However, it should be noted that of the several smart tools currently being adopted in the construction industry, BIM is the most prominent and widely used based on the extant literature. Further research studies can focus on coding the three GBA framework’s process maps as well as conducting real-life assessments of the developed GBA framework on some selected green building projects to confirm its applicability and usefulness. 11.5 Chapter Summary This chapter have presented the major conclusions and recommendations of this study. 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Review of the application of social network analysis (SNA) in construction project management research. International Journal of Project Management, 34(7), 1214–1225. 386 APPENDICES Appendix A: The Documentation for the Building Sustainability Assessment Method (BSAM) Scheme The composition and full documentation for the BSAM scheme as developed in the course of this research http://dx.doi.org/10.17632/jvjm5h8md3.1. can be The BSAM accessed scheme documentation. Figure A1: Cover page of the BSAM scheme documentation 387 is via a – 77-page 389 390 391 392 393 394 Appendix C: Questionnaire Survey Template (Research Objectives #2 – #5) The full sets of the questionnaire survey forms for Objectives #1 (Delphi survey), #2 – #4 as well as the Validation questionnaire surveys for Objectives #3 – #5 can be accessed via – http://bit.ly/2RD2Nlb APPENDIX C1 396 APPENDIX C2 397 APPENDIX C3 398 APPENDIX C4 399 Appendix D: Credit points and Significance value of BSAM scheme criteria Appendix D1: Determination of credit points (score-weights) for the BSAM scheme sustainability criteria Sustainability Indicators 𝑊𝑧 (A) = 11.5 Attributes & subattribute code A1 A11 A12 Attributes & subattribute code E1 E11 E12 Attributes & Sub-attributes Credit Points (CP) Project Site and Design Land Use Integration of cultural heritage in design Construction method 17 3 3 4 A14 A15 A16 Buildability (ease of construction) Enhanced watershed features Reduction of ecological impacts E14 E2 E21 A2 Societal Engagement 3 2 2 8 A21 A22 A23 A24 A3 A31 Engagement of local firms Local employment opportunities Enhanced local economy Public participation Safety & Health Operational safety and wellbeing Reduction of site disturbance 2 2 2 2 7 2 2 E23 E3 E31 E32 E33 E34 Safe neighborhood Space accessibility & availability Reduction of site pollution Ethics & Equity Compliance with labor standards Compliance with social standards 1 1 1 14 2 3 E36 E37 E4 E41 E42 Education & skills development Compliance with safety standards Compliance with client requirements Environmental statutory requirements Construction Material & Waste Locally sourced materials 2 2 2 3 11 2 A13 A32 A33 A34 A35 A4 A41 A42 A43 A44 A45 A46 A5 A51 Sustainability Indicators 𝑊𝑧 (E) = 5.75 E13 E22 E35 E43 𝑊𝑧 (F) = 5.333 400 E44 F1 F11 F12 F13 F2 Attributes & Sub-attributes Sustainable Purchasing Practice Sustainable purchasing plan Ongoing consumables & durable goods Facility alterations and additions & reuse (applied to ‘Existing Buildings’ only) Reduced mercury in Lamps Efficient Use & Selection of Materials Modular and standardized design Using non-ozone depleting substances (nonCFC, non-HCFC) Enhanced refrigerants management Waste Management Practice Solid waste management policy Hazardous waste management Waste stream audit Ongoing consumables & durable goods waste Facility alterations and demolition waste (applied to ‘Existing Buildings’ only) Collection, storage, and disposal of recyclables Waste equipment installation Ease of Conversion of Building Functions Functional adaptation Ease of disassembly (deconstruction) Designing for robustness for asset and landscape Building adaptation strategy plans Alternative Means of Transport Pedestrian & cyclist facilities Reduction of conventional commuting trips Carpooling & vanpooling Community Accessibility Credit Points (CP) 5 1 1 2 1 4 2 1 1 9 1 1 2 1 1 2 1 5 1 1 2 1 7 2 4 1 7 Sustainability Indicators Attributes & subattribute code A52 A53 A54 A55 A6 A61 A62 A63 A64 𝑊𝑧 (B) = 5.0 A65 A66 B1 B11 B12 B13 B2 B21 B22 B23 B24 B3 B31 B32 B33 B34 Attributes & Sub-attributes Credit Points (CP) Construction site waste management Proper material handling Reuse of construction materials Storage facilities & space Project Management 2 2 3 2 12 Project brief & design Engagement of sustainabilityconscious contractors & suppliers Site emergency plan Engagement of sustainabilityconscious project management team Site management plan Commissioning & handover Site Selection Prior green certification (applied to ‘Existing Buildings’ only) Adaptive reuse and preservation of historic landmarks Regional priority Site Management Environmental policy and purchasing plan Environmentally purchasing practices Building exterior and hardscape management plan Landscape management plan Reduction of Heat Island Effect Heat Island reduction in non-roofed areas Heat Island reduction in roof areas Exterior walls finishing materials Consideration of wind movement and building movement and building 3 3 Sustainability Indicators Attributes & subattribute code F21 F22 F3 F31 F32 𝑊𝑧 (G) = 7.0 G1 G11 2 2 G12 1 1 3 1 G14 G15 G2 G13 G21 1 G22 1 7 3 G23 G24 1 2 G26 1 5 1 G31 G32 1 1 1 G4 G41 G25 G3 G33 G42 401 Attributes & Sub-attributes Public transport accessibility Proximity to amenities Transport Management Car parking capacity Provision of low emitting & vehicles Visual Comfort Daylighting and external views fuel-efficient Credit Points (CP) 4 3 2 1 1 8 2 Glare control Interior lighting distribution 1 2 High-frequency ballasts Automatic lighting controls Indoor Air Quality Minimum IAQ performance 1 2 8 1 Environmental tobacco smoke control 1 Adequate cross-ventilation Indoor air quality management Control of greenhouse gases (GHS) emission sources Reduction of light pollution Thermal Comfort 1 2 2 Design and verification Controllability of temperature Thermal comfort in AC & non-AC premises 2 1 2 Acoustic Performance Room acoustics Noise isolation and control 4 1 2 1 5 Sustainability Indicators 𝑊𝑧 (C) = 10.67 Attributes & subattribute code B35 C1 C11 C12 C13 C14 C15 C2 C21 C22 C23 C24 C25 C3 C31 C32 C33 C34 C35 𝑊𝑧 (D) = 6.0 C36 C37 D1 D11 Attributes & Sub-attributes exterior design Greening & ecological enhancement Energy Performance Energy performance targets (applied to ‘Existing Buildings’ only) Energy modeling & reporting (applied to ‘New Buildings’ only) Energy conservation measures Thermal performance of building envelope Compliance with local energy standards Energy Management Energy operating plan Energy monitoring and metering Auditing, commissioning, and testing of energy systems Energy management systems Sustainable maintenance Energy-Efficient Systems Interior lighting efficiency and zoning control Renewable energy systems Energy-efficient circulation systems (lifts, moving walkways, and escalators) Asset’s energy savings (applied to ‘Existing Buildings’ only) Energy efficient appliances and laundry facilities Energy-efficient HVAC equipment Efficient hot water systems Water Efficiency Water recycling & rainwater harvesting Credit Points (CP) Sustainability Indicators 1 10 2 G43 G5 G51 1 G52 2 3 G53 G54 2 G55 10 2 2 3 1 2 12 2 Attributes & subattribute code G6 G61 G62 G63 𝑊𝑧 (H) = 6.5 3 2 G64 H1 H11 H12 H13 H14 1 H15 2 H16 1 1 6 2 H2 H21 H22 H3 402 Credit Points (CP) Attributes & Sub-attributes HVAC background noise Hygiene Plumbing and drainage system separators Chemical leak prevention & storage & liquid 1 9 3 2 Integrated pest management Waste disposal facilities de-odorizing system 1 1 Occupancy comfort survey & feedback (applied to ‘Existing Buildings’ only) Building Amenities Access for persons with disability Amenity features Efficiency of use 2 Low-impact systems and materials Operation & Maintenance Condition survey Staffing quality and resources 2 13 3 1 Building user manual and information Operation & maintenance policy 1 3 Operation and maintenance procedures and manuals Green lease 3 Security Security measures Intruder alarm system Risk Management 3 2 1 7 8 3 2 1 2 Sustainability Indicators Attributes & subattribute code D12 D13 D2 D21 D22 D23 D24 D3 D31 D32 D33 Attributes & Sub-attributes Water-efficient landscaping and irrigation Outdoor water use efficiency Water Management Water conservation plan Water performance monitoring Cooling tower water management Flood and surface management Water Efficient Systems & Equipment Efficient indoor plumbing fixtures & fittings Leak detection system Effluent discharge in foul sewer Credit Points (CP) Sustainability Indicators 3 Attributes & subattribute code H31 1 6 1 2 1 2 6 H32 H33 H34 H4 H41 H42 3 2 1 403 Attributes & Sub-attributes Credit Points (CP) Fire risk assessment 2 Fire risk manager Natural hazards assessment Emergency strategy Green Innovations Innovations in techniques Performance enhancement 1 2 2 3 2 1 Appendix D2: Classification of the significance of each sustainability sub-attribute of the BSAM scheme Sub-attribute code A1 A11 A12 A13 A14 A15 A16 A2 A21 A22 A23 A24 A3 A31 A32 A33 A34 A35 A4 A41 A42 A43 A44 A45 A46 A5 A51 A52 A53 A54 A55 A6 A61 A62 Required (%) Optional (%) Negligible (%) Inference 82.4 34.2 63.8 70.1 52.4 55.6 17.6 60.4 35.7 25.7 43.9 40.6 0 5.3 0.5 4.3 3.9 3.7 R O R R O O 53.5 46.5 59.4 45.5 44.4 48.7 36.4 47.6 2.1 4.8 4.3 7 O O R O 86.2 57.4 70.2 68.6 64.9 13.8 39.9 27.7 26.6 27.1 0 2.7 2.1 4.8 8 R R R R R 78.7 61.2 60.1 65.8 69.7 69.1 17.6 36.7 38.8 33.7 27.1 27.7 3.7 2.1 1.1 0.5 3.2 3.2 R R R R R R 54.3 56.4 69.1 46.8 62.2 45.2 41.5 28.7 49.5 33.5 0.5 2.1 2.1 3.7 4.3 O O R O R 79.3 58 20.2 40.4 0.5 1.6 R O Sub-attribute code E1 E11 E12 E13 E14 E2 E21 E22 E23 E3 E31 E32 E33 E34 E35 E36 E37 E4 E41 E42 E43 E44 F1 F11 F12 F13 F2 F21 F22 F3 F31 F32 G1 G11 404 Required (%) Optional (%) Negligible (%) Inference 74.1 49.2 43.9 34 24.9 47.6 49.7 56.4 1.1 3.2 6.3 9.6 R O O O 67.7 46 49.2 32.3 48.1 43.9 0 5.8 6.9 R O O 67.7 61.9 46.6 51.3 49.7 54.5 49.7 28.6 34.4 46.6 42.9 45 40.2 42.9 3.7 3.7 6.9 5.8 5.3 5.3 7.4 R R O O O O O 66.7 46.6 55.6 56.6 30.7 47.6 42.3 38.6 2.6 5.8 2.1 4.8 R O O R 67.2 41.5 42.3 30.7 56.9 50.3 2.1 1.6 7.4 R O O 70.4 56.6 27 39.7 2.6 3.7 R R 72.3 46.5 25 45.5 2.7 8 R O 81 18 1 R Sub-attribute code A63 A64 A65 A66 B1 B11 B12 B13 B2 B21 B22 B23 B24 B3 B31 B32 B33 B34 B35 C1 C11 C12 C13 C14 C15 C2 C21 C22 C23 C24 C25 C3 C31 C32 C33 C34 Required (%) Optional (%) Negligible (%) Inference 59 58 66.8 59 37.2 39.9 30.5 36.2 3.7 2.1 2.7 4.8 R R R R 62.4 38.6 44.4 31.7 56.6 48.1 5.8 4.8 7.4 R O O 68.8 51.9 48.1 51.6 28 47.1 48.7 45.2 3.2 1.1 3.2 3.2 R O O O 62.8 48.7 60.6 65.4 51.1 31.9 46.5 37.8 31.4 46.8 5.3 4.8 1.6 3.2 2.1 R O R R O 67.7 41.3 55.6 50.3 63 30.2 55 41.3 45.5 31.2 2.1 3.7 3.2 4.2 5.8 R O O O R 64 54 55.6 57.1 64 32.8 40.2 39.7 38.6 32.8 3.2 5.8 4.8 4.2 3.2 R O R R R 70.4 54.5 55 44.4 26.5 42.9 41.3 49.7 3.2 2.6 3.7 5.8 R O O O Sub-attribute code G12 G13 G14 G15 G2 G21 G22 G23 G24 G25 G26 G3 G31 G32 G33 G4 G41 G42 G43 G5 G51 G52 G53 G54 G55 G6 G61 G62 G63 G64 H1 H11 H12 H13 H14 H15 405 Required (%) Optional (%) Negligible (%) Inference 47.6 66 45.2 51.3 50.3 30.9 46.3 44.4 2.1 3.2 8.5 4.2 O R O O 71.1 42.3 68.3 65.1 53.4 54.5 23.5 49.2 30.7 32.8 43.4 39.7 5.3 8.5 1.1 2.1 3.2 5.8 R O R R O R 66.1 54.5 52.9 30.7 40.7 39.7 3.2 4.8 7.4 R O R 64.4 50.8 50.8 31.9 45 44.4 3.7 4.2 22.8 R O O 76.7 56.6 52.4 63.5 48.1 22.8 40.7 43.9 31.2 43.4 0.5 2.6 3.7 5.3 8.5 R O O R O 69.3 56.6 66.1 51.9 27 41.8 31.7 46 3.7 1.6 2.1 2.1 R O R O 78.3 60.3 51.3 61.4 57.7 20.6 39.2 42.9 32.8 37 1.1 0.5 5.8 5.8 5.3 R R O R R Sub-attribute code Required (%) Optional (%) Negligible (%) Inference Sub-attribute code Required (%) C35 47.6 47.6 4.8 O H16 41.3 C36 44.4 50.3 5.3 O H2 C37 44.4 45.5 10.1 O H21 83.6 D1 ` H22 46 D11 64 32.8 3.2 R H3 D12 46 51.3 2.6 O H31 76.2 D13 47.1 43.9 9 O H32 51.9 D2 H33 58.7 D21 67.7 30.7 1.6 R H34 64.6 D22 56.6 40.2 3.2 O H4 D23 42.3 51.3 6.3 O H41 67.2 D24 66.5 27.1 6.4 R H42 58.2 D3 D31 76.6 21.3 21.1 R D32 55.9 39.4 4.8 R D33 62.8 34 3.2 R Note: For column “inference” – R = required attribute; O = optional attribute; N = Negligible attribute. 406 Optional (%) Negligible (%) Inference 51.3 7.4 O 15.3 49.7 1.1 4.2 R O 21.7 41.8 34.9 31.2 2.1 6.3 6.3 4.2 R O R R 29.1 38.6 3.7 3.2 R R Appendix D3: Weight average for the sustainability attributes for the case study projects A1 A2 A3 A4 A5 A6 B1 B2 B3 C1 C2 C3 D1 D2 D3 E1 E2 E3 E4 Weighting average (Project A) 15.5 6 4 5 3.5 3.5 1.5 4 2.5 6.5 9 4.5 3 5 2.5 5 3 6.5 5 Weighting average (Project B) 14 5 6.5 13 5 7 2 4.5 1 6.5 10 5.5 4 5.5 3 3 1.5 9 4 F1 F2 F3 5 7 2 3 4 1.5 G1 G2 G3 G4 G5 G6 H1 H2 H3 H4 3 6.5 2.5 2.75 8 6 6 1 3 1 7 8 4 2.5 9 5 10 3 4 2 Attributes codes 407 Appendix E: Cloud-based Sustainability Decision Support System (Graphical User Interfaces - GUI) APPENDIX E1: Data entry and storage management GUIs Figure E11: "Delete Projects" GUI Figure E12: Sign-in GUI 408 Figure E13: User registration GUI 409 APPENDIX E2: Coding and optimization process GUI Figure E21: Programming codes for the "Compare Projects" GUI Figure E22: HTML codes for the "Delete Project(s)" GUI 410 APPENDIX E3: Coding and optimization process GUI Note: CE duplex (Prj A) and RA labs (Prj B) Figure E31: Comparative assessment of the NB projects respectively on the CSDSS platform – based on the sustainability attributes 411 Figure E32: Comparative assessment of the EB projects respectively on the CSDSS platform – based on the sustainability attributes Note: SNN building (Prj A) and FT building (Prj B). Attributes A1 – A6 is not evaluated for existing buildings according to the BSAM scheme green rating system. 412

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BIM (Architecture)Green BuildingCloud-based web applicationsGreen and Sustainability PracticesLEED SUSTAINABILITY